VOLUME 126 NO. 9 SEPTEMBER 2026
Presidential Address
From Ambition to Action: Delivering Measurable Impact by T.M. Mmola
Introduction to our new President Tshepo Mmola Pr Eng, PMP®, FSAIMM
Tshepo Mmola is a mining engineering professional with 18 years of experience spanning mine operations, academia, and consulting. He is a registered Professional Engineer with the Engineering Council of South Africa (ECSA), a Project Management Professional (PMP®), and a Fellow of the Southern African Institute of Mining and Metallurgy (SAIMM). He holds BSc and MSc degrees in Mining Engineering from the University of the Witwatersrand, as well as a Mine Manager’s Certificate of Competency. Tshepo began his career with Murray & Roberts Cementation, where he gained underground mining experience in gold and platinum group metals, including at South Deep and Karee Mine. He subsequently joined the University of Johannesburg, where he spent more than a decade as a Lecturer and Programme Leader for Mining Engineering. His responsibilities included academic leadership and oversight of the programme’s ECSA accreditation. His academic work included research in mine optimisation and engineering education, with papers published in the SAIMM Journal and presented at conferences locally and internationally. From 2021, Tshepo expanded his involvement in mining consulting as a Director and Principal Consultant at Sound Mining, where he led technoeconomic studies and business process optimisation assignments across 12 countries and a range of commodities, including gold, copper, iron ore, and rare earth elements. In March 2026, Tshepo joined WSP as an Associate Mining Engineer, where he leads client engagements and oversees the technical delivery of mining projects. Tshepo has a long-standing involvement with the SAIMM and has served the Institute in a range of leadership and service roles. He was the founding Chairperson of the SAIMM Young Professionals Council (YPC), serving from 2015 to 2017. He subsequently served on the SAIMM Council and contributed to several Institute structures, including the Technical Programme Committee. His leadership roles have included Chairperson of the Policy, Advocacy and Public Affairs Committee and, more recently, Chairperson of the Membership Committee. His induction as President marks a significant milestone in his SAIMM journey, from inaugural Chairperson of the Young Professionals Council to President of the Institute.
The Southern African Institute of Mining and Metallurgy OFFICE BEARERS AND COUNCIL FOR THE 2026/2027 SESSION President
T.M. Mmola
President Elect M.H. Solomon
Senior Vice President S.J. Ntsoelengoe
Junior Vice President M.C. Munroe
Incoming Junior Vice President A.T. van Zyl
Immediate Past President G.R. Lane
Honorary Treasurer G.R. Lane
Ordinary Members on Council W. Broodryk J.J. Dreyer S.A. Eckstein Z. Fakhraei R.M.S. Falcon (by invitation) B. Genc F. Lake K.M. Letsoalo
S.B. Madolo K.A. Moganedi S.M. Naik G. Njowa T. Randima N. Rampersad S.M. Thema
Co-opted Council Members K.W. Banda M.A. Mello M.L. Wertz
Past Presidents Serving on Council R.D. Beck V.G. Duke I.J. Geldenhuys R.T. Jones W.C. Joughin
E. Matinde C. Musingwini J.L. Porter M.H. Rogers G.L. Smith
M.L. Wertz – TP Mining Chairperson W. Broodryk – TP Metallurgy Chairperson T.S. Ndlela – YPC Chairperson T.B. Mapaire – YPC Vice Chairperson
Branch Chairpersons Chairperson Botswana DRC Johannesburg Limpopo Namibia Northern Cape North West Pretoria Western Cape Zambia Zimbabwe Zululand
M.S. Zulu K. Mosebi K.Y. Janvier A. Hefer T.S. Kgarabjang T.K. Aipanda T. Mashudu T.N.L. Nsimbi P.G.H. Pistorius J. Petersen N.M. Kazembe L. Shamu Vacant
PAST PRESIDENTS *Deceased
* W. Bettel (1894–1895) * A.F. Crosse (1895–1896) * W.R. Feldtmann (1896–1897) * C. Butters (1897–1898) * J. Loevy (1898–1899) * J.R. Williams (1899–1903) * S.H. Pearce (1903–1904) * W.A. Caldecott (1904–1905) * W. Cullen (1905–1906) * E.H. Johnson (1906–1907) * J. Yates (1907–1908) * R.G. Bevington (1908–1909) * A. McA. Johnston (1909–1910) * J. Moir (1910–1911) * C.B. Saner (1911–1912) * W.R. Dowling (1912–1913) * A. Richardson (1913–1914) * G.H. Stanley (1914–1915) * J.E. Thomas (1915–1916) * J.A. Wilkinson (1916–1917) * G. Hildick-Smith (1917–1918) * H.S. Meyer (1918–1919) * J. Gray (1919–1920) * J. Chilton (1920–1921) * F. Wartenweiler (1921–1922) * G.A. Watermeyer (1922–1923) * F.W. Watson (1923–1924) * C.J. Gray (1924–1925) * H.A. White (1925–1926) * H.R. Adam (1926–1927) * Sir Robert Kotze (1927–1928) * J.A. Woodburn (1928–1929) * H. Pirow (1929–1930) * J. Henderson (1930–1931) * A. King (1931–1932) * V. Nimmo-Dewar (1932–1933) * P.N. Lategan (1933–1934) * E.C. Ranson (1934–1935) * R.A. Flugge-De-Smidt (1935–1936) * T.K. Prentice (1936–1937) * R.S.G. Stokes (1937–1938) * P.E. Hall (1938–1939) * E.H.A. Joseph (1939–1940) * J.H. Dobson (1940–1941) * Theo Meyer (1941–1942) * John V. Muller (1942–1943) * C. Biccard Jeppe (1943–1944) * P.J. Louis Bok (1944–1945) * J.T. McIntyre (1945–1946) * M. Falcon (1946–1947) * A. Clemens (1947–1948) * F.G. Hill (1948–1949) * O.A.E. Jackson (1949–1950) * W.E. Gooday (1950–1951) * C.J. Irving (1951–1952) * D.D. Stitt (1952–1953) * M.C.G. Meyer (1953–1954) * L.A. Bushell (1954–1955) * H. Britten (1955–1956) * Wm. Bleloch (1956–1957) * H. Simon (1957–1958) * M. Barcza (1958–1959) * R.J. Adamson (1959–1960) * W.S. Findlay (1960–1961)
* D.G. Maxwell (1961–1962) * J. de V. Lambrechts (1962–1963) * J.F. Reid (1963–1964) * D.M. Jamieson (1964–1965) * H.E. Cross (1965–1966) * D. Gordon Jones (1966–1967) * P. Lambooy (1967–1968) * R.C.J. Goode (1968–1969) * J.K.E. Douglas (1969–1970) * V.C. Robinson (1970–1971) * D.D. Howat (1971–1972) * J.P. Hugo (1972–1973) * P.W.J. van Rensburg (1973–1974) * R.P. Plewman (1974–1975) * R.E. Robinson (1975–1976) * M.D.G. Salamon (1976–1977) * P.A. Von Wielligh (1977–1978) * M.G. Atmore (1978–1979) * D.A. Viljoen (1979–1980) * P.R. Jochens (1980–1981) * G.Y. Nisbet (1981–1982) A.N. Brown (1982–1983) * R.P. King (1983–1984) J.D. Austin (1984–1985) * H.E. James (1985–1986) H. Wagner (1986–1987) * B.C. Alberts (1987–1988) * C.E. Fivaz (1988–1989) * O.K.H. Steffen (1989–1990) * H.G. Mosenthal (1990–1991) R.D. Beck (1991–1992) * J.P. Hoffman (1992–1993) * H. Scott-Russell (1993–1994) J.A. Cruise (1994–1995) D.A.J. Ross-Watt (1995–1996) N.A. Barcza (1996–1997) * R.P. Mohring (1997–1998) J.R. Dixon (1998–1999) M.H. Rogers (1999–2000) L.A. Cramer (2000–2001) * A.A.B. Douglas (2001–2002) * S.J. Ramokgopa (2002-2003) T.R. Stacey (2003–2004) F.M.G. Egerton (2004–2005) W.H. van Niekerk (2005–2006) R.P.H. Willis (2006–2007) R.G.B. Pickering (2007–2008) A.M. Garbers-Craig (2008–2009) J.C. Ngoma (2009–2010) G.V.R. Landman (2010–2011) J.N. van der Merwe (2011–2012) G.L. Smith (2012–2013) M. Dworzanowski (2013–2014) J.L. Porter (2014–2015) R.T. Jones (2015–2016) C. Musingwini (2016–2017) S. Ndlovu (2017–2018) A.S. Macfarlane (2018–2019) M.I. Mthenjane (2019–2020) V.G. Duke (2020–2021) I.J. Geldenhuys (2021–2022) Z. Botha (2022-2023) W.C. Joughin (2023-2024) E. Matinde (2024-2025) G.R. Lane (2025-2026)
Editorial Board S.O. Bada P. den Hoed I.M. Dikgwatlhe M. Erwee B. Genc A.J. Kinghorn D.E.P. Klenam D.F. Malan D. Morris P.N. Neingo S.S. Nyoni M. Onifade M. Phasha P. Pistorius P. Radcliffe N. Rampersad Q.G. Reynolds I. Robinson S.M. Rupprecht
VOLUME 126 NO. 9 SEPTEMBER 2026
Contents Journal Comment: One institute. One purpose. One common goal. by R.M.S. Falcon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Profiles of the Branch Chairpersons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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President’s Corner: From ambition to action: Delivering measurable impact by T.M. Mmola. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Past President’s serving on the Editorial Board R.D. Beck R.T. Jones W.C. Joughin C. Musingwini T.R. Stacey
International Advisory Board members R. Dimitrakopolous R. Mitra S. Ndlovu A.J.S. Spearing E. Topal D. Tudor F. Uahengo D. Vogt
Editor/Chairperson of the Editorial Board R.M.S. Falcon
THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS. Copyright© 2026 by The Southern African Institute of Mining and Metallurgy. All rights reserved. Multiple copying of the contents of this publication or parts thereof without permission is in breach of copyright, but permission is hereby given for the copying of titles and abstracts of papers and names of authors. Permission to copy illustrations and short extracts from the text of individual contributions is usually given upon written application to the Institute, provided that the source (and where appropriate, the copyright) is acknowledged. Apart from any fair dealing for the purposes of review or criticism under The Copyright Act no. 98, 1978, Section 12, of the Republic of South Africa, a single copy of an article may be supplied by a library for the purposes of research or private study. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior permission of the publishers. Multiple copying of the contents of the publication without permission is always illegal. U.S. Copyright Law applicable to users In the U.S.A. The appearance of the statement of copyright at the bottom of the first page of an article appearing in this journal indicates that the copyright holder consents to the making of copies of the article for personal or internal use. This consent is given on condition that the copier pays the stated fee for each copy of a paper beyond that permitted by Section 107 or 108 of the U.S. Copyright Law. The fee is to be paid through the Copyright Clearance Center, Inc., Operations Center, P.O. Box 765, Schenectady, New York 12301, U.S.A. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale.
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Directory of Open Access Journals
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PROFESSIONAL TECHNICAL AND SCIENTIFIC PAPERS
Underground mine stope boundary optimisation using a hybrid network flow-particle swarm algorithm by A. Esmaeili, J. K. Hamidi, A. Mousavi.......................................................................................................................... This study introduces a new hybrid algorithm designed to optimise stope boundaries in sublevel-stoping mines. Stopes were delineated based on the raises’ locations and relevant constraints. Particle swarm optimisation was used to determine the optimal locations of raises. The hybrid algorithm was implemented on a copper deposit in Chile at a depth of 1,000 metres below the surface. The findings are promising, particularly given the growing prevalence of high-tonnage, low-grade deposits in the mining industry. Integrating Bayesian, ensemble, and hybrid machine learning models for predicting overbreak and underbreak in underground stope mining by S.Taheri, E.Topal, R.S. Faradonbeh, H.Jang................................................................................................................. This study employs machine learning methods to predict overbreak and underbreak at individual drillhole locations enabling dynamic prediction of stope shapes before blasting in underground mining. Based on a comprehensive database collected from a longhole sublevel stoping mine in Western Australia, different machine learning-based models, random forest regression, gradient boosting machine, and Gaussian process regression, were developed systematically. Assessment of coal spontaneous combustion potential using statistical and machine learning approaches by H. Ghasemzadeh, H. Madani, F. Sereshki, S. Afraei................................................................................................... This study presents a data-driven framework for evaluating coal spontaneous combustion potential based on intrinsic coal properties derived from proximate and maceral analyses. A combination of statistical and intelligent modelling approaches was employed. The proposed approach provides a practical and interpretable tool for assessing coal spontaneous combustion potential, with potential applications in mine design, operational planning, and post-closure risk management. A critical appraisal of the deformation-based support design approach by J. Wesseloo........................................................................................................................................................................ Empirical approaches, numerical modelling, and analytical methods are employed in ground support design for rockburst conditions, but only analytical approaches attempt to quantify the safety margins of a design. This paper provides an independent appraisal of the deformation-based support design method. The resolution of the identified issues is necessary to demonstrate that the deformation-based support design methodology satisfies the standards expected of a robust engineering approach. Scraper winch safety and alternative cleaning technologies in South African narrow reef hard rock mines by M. Mpofu, T.J. Otto, S. Ngobese, N. Z. Nkosi, F. Magweregwede, N. Tlotleng...................................................... Scraper winches remain the primary machinery for moving broken rock in underground narrow reef hard rock mines in South Africa, but significant safety hazards and risks remain. A review of scraper winch technologies revealed that most potential solutions were previously recommended but have been constrained. The solutions focus directly on enhancing the scraper winch system or replacing it with alternative cleaning solutions, but their economic viability remains challenging.
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One institute. One purpose. One common goal.
ent Comm
A
s we commence a new financial year, I would like to take this opportunity to reflect on the year that has passed and, more importantly, to look ahead with renewed energy, commitment, and optimism.
The past year has seen a whirlwind of exciting new visions, new proposals, new developments, new structures and, not least of all, renewed vigour within a wide range of activities in the different branches within the SAIMM. So, in this new financial year, we not only face virtually a new chapter for the Southern African Institute of Mining and Metallurgy (SAIMM) but we also welcome in our next new President whose role it will be to provide the leadership, vision, and direction that will continue to guide the Institute through the consolidation of the proposals and developments proposed of last year. Amongst those proposals is one to enhance the structure of the Publications arm of the Institute. The plans are to do this by expanding its structure, adding to its staff, enhancing its publishing activities through modern technology, and expanding its outreach in order to consolidate and develop further its role as one of the top journals covering the disciplines of mining and metallurgy in the world. To achieve this, the proposal is to enhance the roles of the publishing team, on one hand by appointing an executive editor to the Editorial Board and, on the other hand, by formalising a standalone Publications Steering Committee (currently operating as the Publications Committee Task Team), which will be led by a chairman. The Editorial Board will expand its activities by exploring new avenues to become more selective in order to enhance or improve the calibre of the Journal’s papers accepted for publication, to ensure that reviewers will be of the highest calibre and appropriately selected for each paper, and that turnaround times in the reviewing and publishing process will be faster and, thus more professional than has been the case to date. In addition, the Editorial Board will be required to consider books submitted to the SAIMM Publishing Department for publication while also promoting those books that have already been published when appropriate, using various methods and selling at various venues. The roles of the Publications Steering Committee will be to promote the outreach of the journal ensuring all stakeholders are approached and encouraged to present papers reflecting the latest advancements in their mining, mineral, and metallurgical sectors. This will include links with allied professional bodies, universities, research organisations, government departments, SOEs, and a wide range of operating mining, mineral, and metallurgical industries. Linking with other activities within the SAIMM is of equal importance. Close association of the Publications Steering Committee, for example, with the Technical Programmes Committee has given rise to ideas for a new conference topic on occasion, whereas papers from running conferences have provided valuable specialist papers and themes for the journal. Future associations with other SAIMM departments lend themselves to further potentially valuable mutually beneficial interaction, especially in the cases of the Young Persons Council, the Academy, and the ESG Committee. With new beginnings come new responsibilities and, undoubtedly, many tasks at hand. The success of the Institute depends not on the efforts of one individual or one committee, but on the collective contribution of all involved. The SAIMM has a proud history and plays an important role in bringing together professionals, academics, researchers, industry leaders, and emerging talent across the mining and metallurgical sectors, both locally and internationally. Maintaining and strengthening this legacy requires collaboration, commitment, and a shared willingness to contribute to the advancement of our disciplines and our Institute.
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Journal Comment Continues
In summary, I would like to extend my sincere appreciation and thanks to all committee members across the Institute – including my own Publications Committee - for the hard work, support, time, and dedication you have contributed and for continuing to grow and participate in this new year. Much of the work undertaken by the committees happens behind the scenes and is little recognised, and yet this is fundamental to the continued functioning and success of the SAIMM. Your willingness to serve, contribute ideas, support initiatives, and give your time is deeply valued. In closing, may I also extend my best wishes to our new President, all office bearers, council members, committee members, staff, volunteers, and members for a successful and rewarding year ahead. May this new chapter be one of progress, productive collaboration, and continued development for the SAIMM and for all those who are involved in it. R.M.S Falcon Editor/Chairperson of the Editorial Board
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SAIMM Branch Chairpersons
Botswana Branch K. Mosebi
Mr Kabelo Mosebi is a Mining Engineer and the Managing Director of Blue Horizon Resources (Pty) Ltd. He also serves as a Director of Pineshare (Pty) Ltd, Galvanizing Botswana (Pty) Ltd and Broad Horizon Group. He has more than 16 years of experience in the mining industry and has held various technical and leadership positions throughout his career. Mr Mosebi holds a Bachelor of Applied Science (BASc) degree in Mineral Engineering (Mining) from the University of Toronto, Canada, which he obtained in 2010. He is currently leading the establishment of the first hot-dip galvanizing plant in Botswana. From 2010 to 2021, Mr Mosebi worked for Debswana Diamond Company and AECI Mining at Jwaneng Mine, where he gained extensive experience in mining engineering, mine planning and operational management. During this period, he participated in the Final Wall Control Project, which improved the final wall compliance score from approximately 20% to 90%. In 2015, Mr Mosebi joined Debswana, Jwaneng Mine, as a Mining Engineer and progressed through various positions to become Mining Manager before leaving the organisation to establish his own business. He was involved in the execution of the Dip Slope Mining project, which contributed to the stability of the Cut 8 slopes and represented a unique mining method. He was also part of the team that presented a technical paper on Dip Slope Mining to an international audience at the Slope Stability Symposium in Spain in April 2018. Prior to this, Mr Mosebi was involved in the redesign of Cut 8 and the optimisation of the slopes. The outcomes of this work contributed to safeguarding the Cut 8 business case at Jwaneng Mine. Mr Mosebi has a strong commitment to community development and youth empowerment. During his studies in Canada, he was actively involved in several campus and community organisations, including serving as Vice-President of the University of Toronto chapter of the National Society of Black Engineers (NSBE) from 2009 to 2010. In Botswana, Mr Mosebi co-founded the Youth Empowerment Society (YES), which was registered with the Botswana Registrar of Societies in April 2008. YES focuses on empowering young people between the ages of 13 and 35, both in and outside of school communities, by addressing challenges relating to educational performance, employability skills, youth violence, and HIV and AIDS. Mr Mosebi is passionate about leadership, mentorship and public speaking, with a particular interest in inspiring young people to realise their potential and contribute positively to the future of their communities and country. He is actively involved in the establishment of the SAIMM Botswana Branches and remains a member of the Southern African Institute of Mining and Metallurgy (SAIMM) and the Australasian Institute of Mining and Metallurgy (AusIMM). u
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SAIMM Branch Chairpersons
DRC Branch K.Y. Janvier
Metallurgical Engineer with expertise in mineral processing, hydrometallurgy, and pyrometallurgy, complemented by strong experience in mining process design, comminution circuit optimisation, equipment reliability, process simulation and modelling, safety, and technical documentation. Focused on delivering efficient, sustainable, and innovative solutions that support the modernisation of mining operations across Africa. A strong advocate for mentorship, collaboration, and knowledge-sharing, with a commitment to advancing operational excellence and developing technical capability within the mining and minerals industry.
Biography Metallurgical Engineer specialising in advancing mining processes through efficiency, safety, reliability, and sustainable innovation. Combines strong technical capability with analytical rigour, practical field experience, and a disciplined and resilient approach to solving complex operational challenges. Deeply grounded in the realities and traditions of African mining while embracing modern technologies and innovative approaches to process development and optimisation. A practitioner and innovator committed to delivering technically sound and practical solutions that improve operational performance and contribute to the long-term advancement of the mining and minerals sector.
Education Metallurgical Engineering background specialising in mineral processing, hydrometallurgy, and pyrometallurgy, with academic training in the design and optimisation of mineral extraction and processing systems. Developed a strong combination of theoretical knowledge and practical problemsolving skills, providing a solid foundation for applying process simulation, modelling, and sustainable technologies to complex challenges in mining and mineral processing.
Professional Career My professional journey has focused on mining process design, comminution circuit optimisation, and process equipment reliability, with extensive experience in improving the performance of crushing and milling systems. I apply process simulation, modelling, and engineering analysis to identify opportunities for optimisation, improve operational performance, and support reliable decision-making. My experience also encompasses safety leadership and technical documentation, ensuring that mining operations are efficient, safe, well-structured, and aligned with sound engineering practices. Beyond technical delivery, I am passionate about contributing to the modernisation, sustainability, and long-term advancement of mining practices, particularly across diverse African mining contexts. I strongly believe that collaboration, professional exchange, mentorship, skills-transfer workshops, and research are fundamental to building a progressive mining industry. By connecting professionals, sharing knowledge, and encouraging continuous learning, I aim to help create an environment where innovation can thrive and the next generation of mining professionals can contribute meaningfully to the advancement of the sector. u
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SAIMM Branch Chairpersons
Johannesburg Branch A. Hefer
Amanda Hefer is a metallurgical engineer with over two decades of experience, particularly in metallurgical laboratories and refineries in precious metals. She currently serves as the Senior Manager of Metallurgical Services at Rand Refinery (Pty) Ltd, where she has held various positions since July 2008. Throughout her tenure, she has reached numerous milestones, including the establishment of a stateof-the-art metallurgical laboratory. She aimed to set up a comprehensive facility that encompasses all fields of metallurgy related to refining, fabrication and smelting processes. Her efforts in implementing cuttingedge equipment and technologies led to higher technical standards and accelerated understanding in these areas. While continually contributing to the industry, Amanda is busy completing her MSc thesis[HS1.1] at the University of Witwatersrand. Additionally, she actively participates in industry conferences and research, shares her knowledge and insights with peers, and mentors the next generation of engineers. Amanda is dedicated to fostering a technologically advanced and collaborative environment in the workplace and building relationships with tertiary institutions for technical and academic input within the metallurgy field. u
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SAIMM Branch Chairpersons
Limpopo Branch T. Kgarabjang
Mr Thabo Kgarabjang is a geologist and academic in the Department of Geology and Mining at the University of Limpopo. He holds an MSc in Economic Geology and is currently pursuing a PhD focused on the genesis of gold and antimony deposits in greenstone belts. His professional and research interests include ore deposit geology, exploration geochemistry, mineral systems analysis, ore-forming processes, and metallurgical extraction and beneficiation. His research encompasses a range of commodities, including base metals, gold, antimony, platinum-group elements (PGE), and phosphate deposits. Mr Kgarabjang maintains active engagement with the mining industry through professional platforms and industry forums. He has served as a panellist at the Investing in African Mining Indaba Disruptive Discussion Stage, contributing to the discussion titled “What does an explorer of the future look like? New approach, new investment opportunities?” He is a member of the Southern African Institute of Mining and Metallurgy (SAIMM), Geological Society of South Africa (GSSA), Society for Geology Applied to Mineral Deposits (SGA), International Association on the Genesis of Ore Deposits (IAGOD), and the South African Council for Natural Scientific Professions (SACNASP). Mr Kgarabjang currently serves as Chairperson of the SAIMM Limpopo Branch, where he contributes to the advancement of the mining and metallurgical professions and supports the sustainable development of South Africa’s mining industry. u
Namibia Branch T. Aipanda
Tomas boasts more than 15 years’ experience in open-pit hardrock mining. He has led in various capacities within mining spanning areas such as drilling, blasting, planning (short and long term), and production management. He is currently serving as Mining Manager at Ongopolo Mining in northern Namibia. u
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SAIMM Branch Chairpersons
Northern Cape Branch T. Mashudu
Mashudu Tseisi is the Plant Technical Services Manager at Anglo American Kumba Iron Ore’s Sishen Mine, with more than 20 years of experience in the South African mining industry. He holds a degree in Chemical Engineering, a Postgraduate Diploma in Business Management and Administration, and a Master of Business Administration (MBA). Throughout his career, Mashudu has led and contributed to major initiatives in process optimisation, plant automation, operational excellence, and mine life extension through the application of innovative technologies. His professional focus combines strong technical expertise with a commitment to operational performance, continuous improvement, and sustainable mining practices. Passionate about developing people and promoting technical excellence, Mashudu actively supports knowledge sharing, professional development, and collaboration within the mining and metallurgical community. As Chairperson of the SAIMM Northern Cape Branch, he is committed to strengthening professional networks, fostering technical growth, and contributing to the continued advancement of the mining and metallurgical industries. u
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SAIMM Branch Chairpersons
North West Branch T. Nsimbi
Thandekile Nsimbi is an accomplished Performance Improvement Manager at Impala Platinum’s PGM smelter with nearly two decades of experience in mining and metallurgy. She is a qualified Metallurgical Engineer with extensive expertise in production management, operational excellence, process optimisation, and continuous improvement. Her career spans leadership roles across leading organisations such as Impala Platinum, Jubilee Metals, Afarak Mogale, Samancor, and Arcelor Mittal, where she has successfully driven strategic initiatives, resource optimisation, and efficiency improvements in smelter and plant operations. She has served in committee and council roles including the Furnace Council Committee, FRCP Steering Committee (Hazardous Materials Champion), and the Process Improvement Council, reflecting her strong industry presence and contribution to advancing metallurgical practices. Thandekile holds a BSc (Hons) Applied Science in Metallurgy (University of Pretoria), a BTech in Metallurgical Engineering (TUT), and a National Diploma in Extraction Metallurgy (UJ), complemented by executive training at GIBS and professional certifications such as Six Sigma Green Belt. She is currently studying towards a Postgraduate Diploma in Business Administration at GIBS, further strengthening her strategic management and leadership skills. Recognised for her leadership, problem-solving ability, and technical expertise, she is passionate about fostering a culture of excellence, safety, and sustainability in metallurgical operations. Her vision is to drive innovation and deliver long-term value in the mining and metals sector. u
Pretoria Branch P.G.H. Pistorius
Pieter Pistorius was born in what is now Modimolle in the Limpopo Province. He studied at the University of Pretoria and at Sheffield University. He achieved a PhD in phase transformation behaviour of 12%Cr steels at the University of Pretoria. He held various positions in the manufacturing industry before joining the Department of Materials Science and Metallurgical Engineering at the University of Pretoria for a second time in 2015. He is responsible for undergraduate and postgraduate teaching and research in welding engineering. He is a fellow of the SAIMM, a registered professional metallurgical engineer and an international welding engineer. u
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Western Cape Branch J. Petersen
Professor Jochen Petersen is a highly experienced chemical engineer, academic, researcher, and metallurgist with more than 25 years of research and university experience, specialising in hydrometallurgy, mineral processing, bioleaching, heap leaching, metal recovery, and sustainable resource extraction. He is currently a Professor in Chemical Engineering at the University of Cape Town (UCT) and has held several senior leadership positions, including Deputy Dean for Postgraduate Studies, Director of the Minerals to Metals Initiative, and former NRF SARChI Chair in Mineral Beneficiation.
Academic and research achievements Professor Petersen has an extensive international research profile, with 99 peer-reviewed journal publications, 87 peer-reviewed conference papers, 8 book chapters, 2 patents and an h-index of 40. He has supervised 41 completed postgraduate students as primary supervisor and continues to supervise PhD and MSc students. He was Editor-in-Chief of the Journal Hydrometallurgy from 2013–2020, reflecting his standing in the international hydrometallurgy community. He has also delivered numerous invited keynote lectures internationally, including in Canada, Japan, China, Chile, the United States and Europe. His work has a strong international dimension, with visiting and adjunct academic appointments in Australia and China, as well as collaborations with universities and research organisations across Europe, Asia and Africa. He also has significant industry-facing research experience, having collaborated with organisations including Rio Tinto, Codelco, Impala Platinum, Afrimat and Ionic Rare Earths on practical problems involving copper, phosphate, PGM and rare-earth processing.
Current research direction His research has evolved from bioleaching and heap-leach modelling toward broader sustainable approaches to metal extraction and recycling. Current interests include e-waste recycling, rare-earth recovery, PGM and copper extraction, alternative leaching chemistries, mineral carbonation, circular economy technologies, and the modelling of reaction and transport processes. In short, Professor Petersen is a senior international expert in hydrometallurgy and sustainable mineral and metal processing, combining strong academic credentials with extensive industrial experience. His particular strength is bridging fundamental chemical-engineering research, mathematical modelling and practical mineral-processing applications, with an increasing focus on resource sustainability, recycling and the circular economy. u
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Zambia Branch N.M. Kazembe
Nathan Mukulakasuba Kazembe is a professional mining engineer, having started his mining career in the Zambian Copper Industry in both underground and open pit. He subsequently moved into projects. Nathan has over 15 years’ experience in projects in the mining industry in Zambia, DRC, and Tanzania. His focus and expertise is project engineering and operations management. Nathan is a Fellow of the Engineering Institute of Zambia (EIZ), and Member of the Charted Institute of Arbitrators (CIArb). He is a Member of the Southern African Institute of Mining and Metallurgy (SAIMM) and Association of Consulting Engineers (ACEZ). Nathan is currently the Country Manager for Zambia for Rolls Royce Solutions Africa, past Operations Manager for Master Drilling Zambia, and is the Director and Co-founder of K-Squared PD Limited. He is past Chairperson of the Kitwe Branch of the Engineering Institute of Zambia, as well as past Member of the F&A Committee of the EIZ. He is currently serves on the Engineering Fund Committee as vice chairperson. Nathan holds a Bachelors’ degree in Mining Engineering (UNZA), PGDip in Project Management from the University of Stellenbosch Business School (USB), Master’s degree in Business Administration (UNZA), and is pursuing his MSc in Mining Engineering part-time. He is a married to Stephanie, has 3 boys, and enjoys rugby, construction, travelling, and community work. He also served as past President of the Roundtable Zambia. u
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Zimbabwe Branch L. Shamu
Mr Shamu is a professionally qualified mining executive with more than 30 years of experience in developing and commissioning new operations, as well as managing mature and established mining operations. He currently serves as Head of Technical at Mimosa Platinum Mine in Zimbabwe. His extensive experience spans project management, the development and commissioning of new mining projects, and the management of mature and established mines. Throughout his career, he has held a range of operational and technical positions, progressing to senior leadership and executive roles within the mining and minerals industry. He has extensive experience in the mining and processing of platinum group metals, base metals and chrome, gained through work across mining operations, smelters, refineries and new development projects. His management expertise has been further strengthened through leading multidisciplinary teams and working in diverse and multicultural environments, both locally and regionally. He serves on several industry boards for tertiary institutions in Zimbabwe and is currently Chairperson of the Board for the Department of Chemical Engineering at the National University of Science and Technology (NUST), Zimbabwe. He is also the current Chairperson of the SAIMM Zimbabwe Branch and serves on a committee of the Zimbabwe Chamber of Mines. He is a member of the Zimbabwe Institution of Engineers and the Southern African Institute of Mining and Metallurgy (SAIMM) and is a registered Professional Engineer with the Engineering Council of Zimbabwe. u
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Presidential Address: From ambition to action: Delivering measurable impact by T.M. Mmola1
Affiliation: 1. WSP, South Africa Correspondence to: T.M.Mmola Email: president@saimm.co.za Dates: Received: 8 October, 2026 Revised: 9 October, 2026 Published: September 2026 How to cite: Mmola, T.M. 2026. Presidential Address: From ambition to action: Delivering measurable impact. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 9, pp. 503 – 510
Abstract
Ambition, while necessary, is insufficient on its own. Material change depends on whether ambition is translated into purposeful action, whether that action takes place within an enabling system, and whether its effects can be demonstrated as meaningful impact. Drawing on experience from the Southern African Institute of Mining and Metallurgy, and in particular the development of the Young Professionals’ Council, the argument is organised around five linked ideas: ambition, the enabling conditions for purposeful action, the distinction between activity, output, outcome and impact, the role of time in the development of impact, and the importance of measurement, learning and adaptation. Professional institutions should therefore judge their success not primarily by the volume of activity they generate, but by the extent to which they strengthen capability, widen participation, support knowledge exchange, and contribute to meaningful and measurable change in the professions and sectors they serve.
Keywords
ambition, measurable impact, purposeful action, institutional effectiveness, organisational learning, adaptation, enabling conditions
DOI ID: https://doi.org/10.17159/2411-9717/2026/2026
Introduction To serve a professional institute with a history extending back to 1894 is to inherit more than institutional continuity. It is to inherit a body of professional knowledge, technical contribution, and accumulated responsibility, as well as the obligation to ask how such an institution remains relevant as the profession and the industry around it continue to change. One period in the recent history of the Southern African Institute of Mining and Metallurgy (SAIMM) particularly shaped my thinking about that responsibility. More than a decade ago, SAIMM's Career Guidance and Education (CGE) Committee was doing important work in encouraging learners and students to enter mining and metallurgy and in supporting their transition into the profession. Over time, however, it became clear that the challenges facing young professionals extended well beyond career guidance alone. Students needed access to practical experience, graduates faced uncertainty about employment, and young professionals needed mentoring, professional development, and more meaningful pathways for participation within the Institute itself. What emerged in response was an ambitious institutional idea: not simply to expand an existing committee, but to create a structure through which young professionals could identify their own priorities, participate meaningfully in the affairs of the Institute, and take responsibility for developing and delivering programmes that addressed their needs. That process ultimately led to the creation of the Young Professionals' Council (YPC). Looking back, what matters most is not merely that a new set of activities was established, but what that period can teach about the relationship between ambition, execution, and meaningful institutional change. The central argument is straightforward. Ambition matters, but ambition does not produce change on its own. It must be converted into purposeful action, and that action must ultimately be judged not by the volume of activity it generates, but by whether it produces meaningful change in the condition it was intended to address.
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Presidential Address: From ambition to action: Delivering measurable impact The argument develops through five linked propositions. First, ambition defines the future condition worth pursuing. Second, action depends on the enabling conditions within which execution takes place. Third, activity must be distinguished from impact. Fourth, impact develops over time and may do so in different ways. Fifth, measurement only becomes useful when it feeds learning and adaptation. These propositions are then considered in relation to SAIMM as a professional institution and illustrated through the case of the Young Professionals' Council. This experience-based conceptual reflection proposes a practical framework for assessing how professional institutions convert ambition into measurable impact. It is intended to be analytically useful and relevant to professional institutions and the wider minerals industry. Its purpose is to support a more disciplined response to a simple but demanding question: What changed because we acted?
Ambition: Defining the change we seek Ambition is a statement about the future we are trying to create. It gives direction by defining the change we seek. In that sense, ambition is not simply aspiration. It is a disciplined attempt to describe a future condition that does not yet fully exist, but that has been judged worth pursuing. In mining and metallurgy, a number of ambitions are expressed with particular clarity, including zero harm, net-zero emissions, a just energy transition, local content, beneficiation and value addition, and technology-enabled mining. Zero harm remains one of the defining ambitions in health and safety. It is more than a slogan or numerical target. It expresses an expectation that mining and metallurgical work should be organised, led, and improved in ways that make fatalities, injuries, and occupational disease progressively less likely (De Jager, 2018; Joughin, 2024). Net-zero emissions and the just energy transition express ambitions to decarbonise mining and metallurgical systems while maintaining competitiveness and responding to the social consequences of transition. For Southern African producers, this challenge cannot be separated from energy availability and affordability or from the employment, skills, and livelihoods of the workers and communities most exposed to change. Local content, beneficiation, and value addition reflect the ambition to retain a greater share of mineral value within the countries and regions where it is produced and for mining to create durable value in the communities in which it operates. The energy transition and supply-chain resilience have renewed attention on the minerals required for modern technologies. Southern Africa's opportunity lies not only in supplying these minerals, but in using its geological endowment, technical expertise, and infrastructure to create more value from them. South Africa's Critical Minerals and Metals Strategy links this ambition to exploration, localisation, research and innovation, skills, infrastructure, and energy security (Department of Mineral and Petroleum Resources, 2025). Understood in this way, the ambition concerns deepening industrial capability, not downstream processing alone. There is also a strong ambition around technology-enabled mining. Automation, digital systems, artificial intelligence, advanced process control, and remote or data-driven operating models are being pursued globally as means to improve safety, decision-making, productivity, environmental performance, and competitiveness. The success of this ambition will depend not 504
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only on the technology itself, but also on the capability, leadership, systems, and learning culture needed to embed it in practice. SAIMM has articulated an ambition of its own. It seeks to become a more integrated, responsive, and sustainable professional system, better able to serve its members and the profession as the world around it changes (Lane, 2026). That ambition is consistent with the Institute's broader role in supporting access to technical and professional knowledge in mining, metallurgy, and related sectors (Southern African Institute of Mining and Metallurgy, 2019). These ambitions differ in scale and purpose, but they share an important characteristic. Each describes a condition that does not yet fully exist but that serves as a point of direction. That is the value of ambition: it clarifies intention, establishes orientation, and gives purpose to action. The difficulty, however, is that ambition can be rhetorically strong while still being operationally weak. A well-phrased ambition may inspire, align, or signal seriousness of intent, but it does not by itself change systems, develop people, improve performance, or alter institutional behaviour. This is why some ambitions gain momentum while others remain little more than plans. What separates them is seldom the quality of the ambition itself, but the conditions under which it is pursued.
Enablers of purposeful action If ambition does not produce change on its own, the next question is what enables ambition to become purposeful action. I propose seven interrelated conditions that are particularly important: people, governance, systems, capability, resources, technology, and culture. I refer to these as the enablers of purposeful action (Figure 1). These should not be treated as sequential steps or as a simple checklist. They operate simultaneously, interact continuously, and vary in relative importance depending on the nature of the ambition being pursued. A weakness in any one of them may constrain execution, even where the ambition is sound and the intent to act is genuine. The contribution of each enabler to purposeful action is considered in what follows here. People interpret information, exercise judgement, make decisions, accept responsibility, and coordinate effort. Even a technically sound initiative will struggle if those expected to deliver it do not understand its purpose, are not sufficiently engaged, or lack the authority to act. Governance determines how authority, responsibility, and accountability are structured. It establishes the legitimacy through which action occurs. Good governance provides necessary discipline, but it must also enable appropriate and timely decisions. Where responsibilities are unclear or decision-making structures no longer suit the problem being addressed, execution can be constrained. Systems are the processes, routines, workflows, and information flows through which work is coordinated. They determine how decisions are translated into action across organisational boundaries. Failures of execution are therefore not always failures of intent or competence; they may arise because information does not reach the right people, processes create unnecessary friction, or responsibilities fall between established structures. Capability concerns whether the knowledge and skills required to perform the work exist. Technical competence remains fundamental in mining and metallurgy, but increasingly, it must be complemented by managerial, digital, commercial, and The Journal of the Southern African Institute of Mining and Metallurgy
Presidential Address: From ambition to action: Delivering measurable impact
Figure 1—Enablers of purposeful action
interpersonal capability. An organisation cannot reasonably expect new forms of performance without developing the capabilities required to deliver them. Resources determine what can practically be delivered and sustained. These include capital and funding, but also time, infrastructure, specialist expertise, and institutional capacity. An initiative may be strategically important and technically credible, but its execution will remain constrained if the resources required to sustain it are unavailable. Technology can extend capability, improve decisions, reduce exposure to risk, increase efficiency, and remove constraints that were previously difficult to overcome. But technology is an enabler, not an objective in itself. Its value lies not simply in implementation, but in whether it improves the condition it was introduced to address.
Finally, culture shapes the behaviours within which all the other enablers operate. It influences what people are willing to challenge, how accountability is exercised, whether uncomfortable information is surfaced, how failure is treated, and whether learning leads to change. Culture may be less tangible than a system or a resource allocation, but its effect on execution can be substantial. The importance of these seven enablers lies not only in their individual contribution, but in their interaction. Technology without capability may be underused. Capability without resources may remain dormant. Resources without governance may be misdirected. Well-designed systems may still underperform in a culture that discourages challenge or accountability. And committed people can achieve only so much when the structures around them repeatedly make effective action difficult. This leads to an important proposition:
Table 1 The seven enablers of purposeful action: Diagnostic questions and potential constraints Enabler
Description
Core question
Illustrative constraint
People
Agency, leadership, judgement, accountability, and engagement. Authority, decision rights, accountability, and legitimacy. Processes, workflows, routines, and information flows. Technical, managerial, digital, commercial, and interpersonal knowledge and skills. Capital, funding, time, infrastructure, and institutional capacity. Tools that extend capability, improve decisions, reduce risk, or increase efficiency. Behaviours, norms, openness to challenge, learning, and adaptability.
Are the right people engaged, authorised, and accountable? Does the decision structure support timely action? Do processes and information flows support execution? Are the needed technical and organisational skills available? Is there sufficient time, funding, capacity, and expertise? Does the technology improve the condition being addressed? Do norms support learning, challenge, and accountability?
Limited ownership or unclear authority.
Governance Systems Capability Resources Technology Culture
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Slow or misaligned decisions. Friction, gaps, or hand-off failures. Skills or knowledge gaps. Under-resourcing. Technology without adoption or value. Silence, blame, or resistance to change.
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Presidential Address: From ambition to action: Delivering measurable impact Effective action occurs within an enabling system The quality of execution depends materially on the condition of that system. This understanding has practical consequences for how we respond when execution falls short. The instinct is often to demand more effort, increase activity or introduce another initiative. But if the underlying constraint lies elsewhere in the enabling system, more effort may simply reproduce the same result. The more useful response is to diagnose the constraint (Table 1). Is the ambition clear and understood? Do people have the authority and accountability required to act? Does governance support timely decisions? Are systems aligned with the intended outcome? Does the necessary capability exist? Are sufficient resources available? Is technology solving the right problem? Does the culture support the behaviour required? This does neither mean that all seven conditions must be equally strong before action can occur. Nor does it suggest that execution can ever be made entirely predictable. It means that the quality of action cannot be understood independently of the conditions within which it takes place. Ambition, therefore, is converted into action not simply through effort, but through the alignment of the conditions that make purposeful execution possible. The next question is whether that action actually produces the change intended.
From activity to measurable impact If action is necessary to move from ambition towards delivery, then it is also necessary to be precise about what follows once action has been taken. One of the most common weaknesses in institutional and organisational practice is the tendency to treat visible activity as if it were evidence of meaningful change. Activity is easier to observe than impact, and it is therefore more easily mistaken for success. Four terms need to be distinguished: activity, output, outcome, and impact. An activity is what is done. An output is the direct product of that activity. An outcome is the change that follows from those outputs. Impact is the broader, higher-level change to which outcomes contribute and concerns whether the condition identified in the original ambition has changed in a meaningful way (Belcher, Palenberg, 2018). This relationship can be expressed as a progression from activity to output to outcome to impact (Figure 2). That progression, however, should not be understood as simple or automatic. An activity does not necessarily produce the intended output, an output does not guarantee an outcome, and an outcome does not inevitably translate into meaningful impact. That is why organisations can be very busy without necessarily being effective. Most reporting systems naturally privilege what is easiest
to count: meetings held, funds spent, participants reached, programmes launched, and milestones completed. These measures remain useful because they help establish whether something was done and whether it was delivered at scale. But they are incomplete if they become the primary basis on which success is judged. This distinction guards against equating effort with effectiveness. High levels of activity may demonstrate commitment, and strong outputs may demonstrate competent execution. However, neither establishes that the condition the ambition was intended to address has changed. If impact is to be more than a statement of intent, there must be some basis for determining whether meaningful change has occurred. That shifts attention from effort to consequence, and from delivery to significance. It also forces clarity at the beginning of an intervention, because if the desired change has not been defined with sufficient precision, it becomes difficult to determine later whether meaningful progress was made. In this context, measurable refers to the availability of credible evidence, meaningful refers to relevance to the original ambition, and material refers to the significance of the change achieved. The more demanding discipline is therefore to demonstrate the claimed change rather than merely assert it. In doing so, it is important to avoid equating measurable with quantifiable. Not every meaningful change can be reduced to a single numerical measure, but the evidence used should be sufficiently clear and credible to support a reasoned judgement about whether progress has occurred. Importantly, impact does not require the ambition to have been fully realised. Meaningful change may occur along the way. The relevant question is whether action has materially changed the condition it was intended to address, and whether that change represents progress towards the ambition that was established. We should therefore measure what matters, without allowing what is easy to measure to become what matters. As William Bruce Cameron observed, “Not everything that can be counted counts, and not everything that counts can be counted” (Cameron, 1963).
Time and the development of impact One of the risks in discussing measurable impact is the tendency to equate what is measurable with what is immediate. Meaningful change does not always become visible within the same period in which the underlying activity occurs, and the absence of immediate evidence should not automatically be interpreted as the absence of impact. To understand impact more rigorously, two related but distinct questions need to be considered. The first is when impact becomes visible. The second is how impact develops. These two dimensions are connected, but they should not be conflated. These two dimensions are depicted in Figure 3.
Figure 2—Activity-output-outcome-impact progression relationship 506
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Figure 3—Two dimensions of impact: when it becomes visible and how it develops
In terms of time, impact may be relatively immediate or become evident only over the long term. Immediate impact refers to change that can be observed reasonably soon after an intervention. Some outcomes become visible relatively quickly, and in these cases the relationship between action, outcome, and impact can be reasonably direct. A safety intervention, for example, may reduce exposure to a known hazard, contributing to fewer injuries and improved safety performance. Where these changes are sustained and materially advance the original ambition, evidence of impact may therefore become visible within a relatively short period. Long-term impact becomes evident over a more extended period. This is particularly important in fields such as education, research, professional development, leadership formation, and institutional capability, where some of the most consequential effects may take years to become visible. Time, however, tells only part of the story. It is also necessary to consider how impact develops. Cumulative impact results from repeated or sustained action of which its effects build over time. Safety improvement provides a useful example. Progress is seldom attributable to a single intervention. It accumulates through engineering improvements, operating practice, research, regulation, training, leadership, and sustained attention. Interconnected impact arises where eventual change cannot reasonably be attributed to one action in isolation because several influences interact. Professional capability, for example, may be shaped simultaneously by formal education, workplace experience, mentoring, technical knowledge, professional networks, and opportunities to assume responsibility. Emergent impact refers to consequences that arise from an action, or combination of actions, but could not have been fully specified or predicted at the outset. Research undertaken for one purpose may later find application elsewhere. An institutional initiative may create opportunities that were not part of its original design. These dimensions are not mutually exclusive. An impact may be immediate and interconnected, long-term and cumulative, or cumulative in a way that eventually produces emergent effects. This understanding has direct implications for measurement. If every intervention is judged against the same short reporting The Journal of the Southern African Institute of Mining and Metallurgy
cycle, actions of which their benefits require time to develop may be undervalued. Conversely, long time horizons and complex causal relationships should not become a licence for vague claims unsupported by evidence. Therefore, the measures we select, and the period over which change is assessed, must be appropriate to the impact we are trying to understand.
Learning and adaptation Measurement has limited value if it ends with recording what happened. Once evidence exists of what changed, the more important task is to interpret that evidence, understand why the observed result occurred, and determine what should happen next. The purpose of measurement is therefore not simply to prove success or identify failure, but to improve the quality of subsequent judgement and action. This is where learning and adaptation become integral to the impact process. The relationship can be understood as a continuing cycle: ambition, action, impact, learning, and adaptation. This cycle is illustrated in Figure 4. In this sense, impact is not the end of the process. It becomes an input into the next round of judgement and execution.
Figure 4—The continuing cycle of ambition, action, impact, learning, and adaptation VOLUME 126
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Presidential Address: From ambition to action: Delivering measurable impact The next step following measurable impact is learning. Learning requires more than recording whether a target was met. It requires an attempt to understand why the observed result occurred. Organisational learning scholars have long argued that learning becomes organisationally meaningful only when insights move beyond individual awareness into shared interpretation, coordinated action, and institutionalised practice (Crossan et al., 1999; Basten, Haamann, 2018). In engineering and mining practice, this is familiar territory. We reconcile plans against actual performance. We investigate variance. We examine whether assumptions were correct, whether conditions changed, and whether the systems we designed behaved as expected. The same discipline should apply to programmes, strategies, and institutions. Where intended impact has been achieved, the task is to understand what contributed to that success and whether it can be sustained or replicated. Where the outcome has fallen short, measurement should not merely record the shortfall. It should help diagnose its cause. That diagnosis is important because underperformance can arise at different points in the chain we have already considered. The ambition itself may have been poorly defined or based on incorrect assumptions. The action may have been inadequate. One or more of the enabling conditions may have constrained execution. The outputs may have been delivered without producing the expected outcomes. External conditions may have changed. Or the impact may simply require more time to become visible. These explanations lead to very different responses. If capability is the constraint, repeating the same action with greater intensity is unlikely to solve the problem. If governance is preventing decisions, additional resources may have little effect. If the underlying assumption was wrong, improving execution may simply enable us to implement the wrong solution more efficiently. The next step is adaptation. Adaptation is the deliberate adjustment of ambition, action, or enabling conditions in response to what has been learned. It may involve strengthening a weak enabler, redesigning a programme, revising a measure, extending the time horizon for assessment, or reconsidering the original objective. Adaptation is not an admission that the original effort failed. In complex systems, some assumptions will prove incomplete and some interventions will behave differently from what was anticipated. The more important question is whether the response to the evidence is intelligent, deliberate, and timely. A useful distinction can therefore be drawn between measurement, learning, and adaptation. Measurement provides evidence of what happened. Learning seeks to understand what contributed to it. Adaptation determines what should be changed, retained, expanded, or stopped. Without learning, measurement produces information but not understanding. Without adaptation, learning has little operational value.
The starting point was a recognised limitation in the Institute's existing approach to early-career development. By 2014, it had become evident that the needs of young professionals extended beyond career guidance alone. Students needed access to practical experience, graduates faced uncertainty about employment, and young professionals needed mentoring, professional development, and meaningful pathways for participation within the Institute itself (Mmola, 2015; Duke, 2016). The ambition that emerged in response was therefore broader than the expansion of an existing committee. It was to create a structure through which young professionals could identify their own priorities, participate meaningfully in the affairs of SAIMM, and assume responsibility for developing and delivering programmes relevant to their stage of professional development. In September 2014, the CGE convened a Workshop on Youth Development at Sci-Bono, and from that process a group of young professionals was entrusted with taking the initiative forward. That process ultimately led to the formation of the YPC, of which its establishment and operating by-law were approved by the SAIMM Council in January 2015 (Mmola, 2015; Duke, 2016). Viewed through the framework proposed earlier, the significance of this case lies in the fact that the initiative should not be judged only by the activity it generated. The more important question is whether it contributed to meaningful change in participation, professional development, institutional inclusion, and leadership formation over time. The YPC illustrates the importance of enabling conditions. Its establishment required a recognised institutional need, sponsorship from the existing CGE Committee and the SAIMM Council, an agreed governance structure, opportunities for young professionals to assume responsibility, and sufficient institutional support to convert an initial workshop into a continuing platform for participation and programme delivery. The effects of the actions to establish the YPC are better interpreted over a longer horizon. As the YPC's inaugural chairperson and now President of SAIMM, I am conscious that my progression could be read as a measure of the YPC's success. It is not presented as such here, nor as a sufficient measure of its institutional value. It is, however, one visible long-term outcome connected to an earlier decision by the Institute to create opportunity, responsibility, and space for young professionals to lead. This case also illustrates the temporal and developmental character of impact. Some effects were immediate, some long-term, some cumulative, some interconnected, and some emergent. The case therefore reinforces the central argument: ambition became meaningful only when it was translated into action through an enabling institutional system, and the significance of the resulting activity lay in what it made possible over time.
The Young Professionals’ Council as an illustrative case
The framework is not intended only as a way of analysing individual programmes. It also offers a way of thinking about the role and responsibility of a professional institution operating within a changing industry. For SAIMM, this matters because the Institute's influence is distinctive and enabling rather than operational. SAIMM does not operate mines, allocate capital, or make production decisions on behalf of the industry. Its contribution
The establishment of the Young Professionals' Council (YPC) within SAIMM offers a useful illustration of the framework advanced here. Its value lies not simply in the historical fact that a new structure was created, but in what that process reveals about the relationship between ambition, enabling conditions, action, and longer-term institutional effects. 508
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Implications for the Southern African Institute of Mining and Metallurgy and professional institutions
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Presidential Address: From ambition to action: Delivering measurable impact lies in the generation and exchange of technical knowledge, the development of professionals, the convening of expertise, the strengthening of professional networks, and the stewardship of an institutional platform through which the disciplines of mining and metallurgy continue to evolve. The more important assessment is therefore not the volume of activity undertaken, but whether that activity strengthens professional capability, supports knowledge transfer, widens meaningful participation, deepens technical exchange, and improves the ability of the profession to respond to changing conditions. This resonates with communities-of-practice and organisational learning thinking, which emphasises knowledge exchange, interaction, and institutionalisation rather than mere information distribution (Crossan et al., 1999; Jørgensen, Edwards, 2019) Before initiating a programme, SAIMM and comparable institutions should be able to state the ambition, identify the required enabling conditions, define the intended outputs and outcomes, specify credible evidence of impact, and establish how the resulting evidence will inform adaptation. The same logic can be applied more broadly to industry ambitions such as zero harm. In South African mining, the pursuit of Zero Harm has repeatedly been described not as a single intervention, but as a systemic and leadership-dependent undertaking requiring engineering controls, operating practice, culture, learning, and sustained institutional commitment over time (De Jager, 2018, 2019; Joughin, 2024). For SAIMM, the practical implication is that “From ambition to action: Delivering measurable impact” should not be treated merely as a presidential theme. It should be understood as an institutional discipline. The test of that discipline will not be how much the Institute sets out to do, but whether its work produces changes that are material to the professions and communities it exists to serve.
Conclusion Ambition, while necessary, is insufficient on its own. Ambition establishes the future condition worth pursuing, but it does not by itself produce change. Meaningful progress depends on whether ambition is converted into purposeful action and whether that action produces material change in the condition it was intended to improve. Three related ideas help to clarify that process. First, action takes place within an enabling system shaped by people, governance, systems, capability, resources, technology, and culture. Second, activity must be distinguished from output, outcome, and impact, because visible effort and delivery are not the same as meaningful change (Belcher, Palenberg, 2018). Third, impact must be understood over time and interpreted through a cycle of learning and adaptation rather than treated as a once-off assessment. The broader implication for SAIMM is that relevance cannot be maintained through activity alone. A professional institute must be clear about the change it seeks to enable, deliberate in the way it acts, rigorous in how it interprets evidence, and willing to adapt where learning requires it.
References
Basten, D., Haamann, T. 2018. Approaches for organizational learning: A literature review. SAGE Open, vol. 8, no. 3, article 2158244018794224. https://doi.org/10.1177/2158244018794224 The Journal of the Southern African Institute of Mining and Metallurgy
Belcher, B.M., Palenberg, M. 2018. Outcomes and impacts of development interventions: Toward conceptual clarity. American Journal of Evaluation, vol. 39, no. 4, pp. 478–495. https://doi.org/10.1177/1098214018765698 Cameron, W.B. 1963. Informal Sociology: A Casual Introduction to Sociological Thinking. Random House, New York, NY, p. 13. Crossan, M.M., Lane, H.W., White, R.E. 1999. An organizational learning framework: From intuition to institution. Academy of Management Review, vol. 24, no. 3, pp. 522–537. https://doi.org/10.2307/259140 De Jager, E.J. 2019. A systems approach to improved safety in the South African gold and platinum sectors, post 2006. PhD thesis, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg. http://hdl.handle.net/10539/29407 De Jager, K. 2018. Safety and health leadership: A concise 'story line' to achieve zero harm. Journal of the Southern African Institute of Mining and Metallurgy, vol. 118, no. 4, pp. 431–437. https://doi.org/10.17159/2411-9717/2018/v118n4a13 Department of Mineral and Petroleum Resources. 2025. Critical Minerals and Metals Strategy South Africa 2025. Republic of South Africa, Pretoria. Available at: https://www.gov.za/documents/other/critical-minerals-andmetals-strategy-south-africa-2025-12-may-2025 Duke, V. 2016. A Council for our Youth. Journal of the Southern African Institute of Mining and Metallurgy, vol. 116, no. 4, pp. iv–v. Available at: https://www.scielo.org.za/pdf/jsaimm/v116n4/01.pdf Jørgensen, R., Edwards, K. 2019. Communities of practice in healthcare: A framework to manage knowledge sharing in operations. Knowledge and Process Management, vol. 26, no. 4, pp. 300–314. https://doi.org/10.1002/kpm.1590 Joughin, W.C. 2024. President's Corner: Quest for zero harm in South African deep gold mines. Journal of the Southern African Institute of Mining and Metallurgy, vol. 124, no. 4, pp. vi–viii. Available at: https://www.scielo.org.za/pdf/jsaimm/v124n4/02.pdf Lane, G.R. 2026. President's Corner: From strengthening an institution to stewarding a system. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 7, pp. v–vi. Available at: https://www.scielo.org.za/pdf/jsaimm/v126n7/02.pdf Mmola, T. 2015. The SAIMM Young Professionals' Council (SAIMM-YPC). Journal of the Southern African Institute of Mining and Metallurgy, vol. 115, no. 6, p. v. Available at: https://www.scielo.org.za/pdf/jsaimm/v115n6/04.pdf Southern African Institute of Mining and Metallurgy. 2019. Constitution and by-laws. SAIMM, Johannesburg. Available at: https://www.saimm.co.za/about-saimm/constitution-and-bylaws. u VOLUME 126
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Underground mine stope boundary optimisation using a hybrid network flow-particle swarm algorithm by A. Esmaeili1, J. K. Hamidi1, A. Mousavi1
Affiliation: 1 Mining Engineering Department, Faculty of Engineering, Tarbiat Modares University Jalal Ale Ahmad Highway Correspondence to: A. Esmaeili Email: ahmarezaesmaili12@gmail.com
Dates:
Received: 23 Jan. 2025 Revised: 06 Nov. 2025 Accepted: 10 Aug. 2026 Published: Sep. 2026 How to cite: Esmaeili A., Hamidi J.K., Mousavi A. 2026. Underground mine stope boundary optimisation using a hybrid network flowparticle swarm algorithm. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 9, pp. 511 – 524 DOI ID: https://doi.org/10.17159/2411-9717/3657/2026 ORCiD: A. Esmaeili https://orcid.org/0009-0004-1816-8300 J.K. Hamidi https://orcid.org/0000-0001-8820-3256 A. Mousavi https://orcid.org/0000-0002-5436-696X
Abstract
This study introduces a new hybrid algorithm designed to optimise stope boundaries in sublevelstoping mines. Stopes were delineated based on the raises’ locations and relevant constraints, including minimum and maximum stope dimensions, stope overlaps, and geotechnical considerations. Particle swarm optimisation was used to determine the optimal locations of raises, while a network-flow algorithm applied constraints and generated stopes surrounding each raise, defining their size and shape. The hybrid algorithm was implemented on a copper deposit in Chile at a depth of 1,000 meters below the surface. A comparative analysis against the network-flow and maximum-value neighborhood algorithms showed that the economic value of stopes produced by the hybrid algorithm exceeds that of the network-flow algorithm by about 4% and exceeds that of the maximum-value neighborhood algorithm by about 7.5%. Moreover, the proposed algorithm was able to produce stable stope layouts for large-scale problems within a reasonable solution time. These findings are promising, particularly given the growing prevalence of high-tonnage, low-grade deposits in the mining industry.
Keywords
Stope boundary optimization, operation research, hybrid algorithm, underground minnig
Introduction The uptrend in the use of underground mining methods, driven by the depletion of surface and nearsurface mineral reserves Copland, Nehring (2016), and the rising costs associated with development, rock breakage, and handling in underground mines have made stope boundary optimisation (SBO) more essential than ever (Nhleko et al., 2018). In underground mining, optimisation of stope boundaries, access networks, and production planning stands out as critical, collectively influencing project profitability. Among these, SBO is particularly significant as it serves as a prerequisite for optimising other critical aspects. To address SBO, researchers have proposed various models that primarily use either mathematical logic or a local-search (heuristic) approach Ataee-Pour (2000). The main concerns with these models are extended solution times and suboptimal outcomes. Hence, a model providing optimal solutions within shorter time frames would be valuable. For this, hybrid algorithm combinations of two or more algorithms have been proposed. Hybrid algorithms have been effectively employed to address a range of mining-engineering problems, including production planning Zaidan et al.(2019), scheduling Lamghari et al.(2015), SBO Nikbin et al. (2019), and pushback design Tolouei et al.(2020). The strong performance of hybrid algorithms in optimisation tasks makes them well suited for solving mining engineering problems, and their increasing adoption by researchers, particularly in recent years, underscores their value. For instance, Nikbin et al. (2019) introduced a hybrid approach combining dynamic programming and greedy algorithms to solve the SBO problem. The results obtained from the proposed model outperformed existing three-dimensional (3D) SBO models. In the present study, sublevel stoping is employed as the mining method for SBO. Sublevel stoping is recognised for its high production efficiency, characterised by accessing the orebody from the side through inclined ramps known as declines and a shaft from the surface Sari and Kumral (2020). This method is specifically designed for the progressive extraction of specified ore blocks between pillars of surrounding material Villaescusa (2014). The optimal stope layout in underground mines represents the area where mining operations yield the highest economic value. While models based on mathematical logic can derive optimal solutions, they often face limitations in accommodating numerous constraints, leading to longer solution times as problem size grows. Various studies Riddle (1977), Ovanic (1998), Jalali and Araee-Pour (2004), and Bai et al. (2013) have introduced mathematically-based algorithms tailored either to specific methods or
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Underground mine stope boundary optimisation using a hybrid network flow-particle limited to one- or two-dimensional (1D or 2D) problems, whereby solution times rise with larger problem volumes. Therefore, heuristic algorithms have been proposed to optimise underground mining by considering a broader range of constraints and reducing runtime. Heuristic algorithms can accommodate more constraints than mathematical models but do not guarantee optimal solutions. Notable examples include those proposed by Alford (1996), Ataee-Pour (2004), Topal and Sens, (2010), Sandanayake et al. (2015), and Matamoros and Kumral (2017). Mmola et al. (2019) introduced a distinct model for SBO, marking the first use of the particle swarm optimisation (PSO) algorithm for this purpose. Their model demonstrates the capability to manage extraction constraints and provides a feasible approach, though it does not guarantee the true optimal solution. Esmaeili et al. (2023) proposed another model focusing on the Mining Rock Mass Rating (MRMR) classification system, which establishes stable stopes and employs the Network Flow Algorithm (NFA) to delineate stope boundaries at each level. Ataee-Pour (2000) introduced a maximum-value neighbourhood (MVN) algorithm, akin to the floating-stope algorithm, which optimises operations by probing neighbouring blocks with the highest economic value while considering stope geometry across one, two, and three dimensions. Recently, some researchers have used hybrid algorithms for solving SBO problems, e.g., studies by Nikbin et al. (2019), Sari and Kumral (2020), and Nikbin et al. (2020). Nikbin et al. (2019) presented a combined dynamic–greedy programming model that addresses complexities in SBO problems by reducing the number of variables and inequalities. Meanwhile, Sari and Kumral (2020) introduced a dynamic–greedy programming hybrid algorithm tailored for sublevel mining area optimisation, aimed at reducing solution time and memory usage for larger problems. Nhleko and Musingwini (2022) introduced the dual interchange algorithm (DIA), a hybrid approach that combines PSO and genetic algorithm (GA) components to generate near-optimal solutions. A hybrid model peresented by Nikbin et al. (2020) uses a greedy heuristic approach to solve the main problem by first transforming selected two-dimensional subregions of the main economic-block model into unit blocks. This reduction converts the original three-dimensional problem into a secondary onedimensional subproblem. A novel dynamic programming algorithm is then applied to determine the optimal boundaries within this subproblem. Among the resulting scenarios, the best one is chosen according to the greedy criterion, and the blocks corresponding to that solution are added back to the solution of the original problem. The model yields a solution that is close to optimal and practically acceptable. However, the approach does not account for geomechanical constraints, cannot generate stopes with varying dimensions, and does not consider the heights of the stopes. Additionally, the presented model is a holistic model, whereas the proposed model is a fine-grained, part-based algorithm. This means that optimisation is performed separately for each level, eliminating the possibility of a stope being located in the middle of a level. Recent advancements in hybrid algorithms for underground mine design and planning problems are summarised in Table 1. As Tolouei et al. (2020) noted, hybrid algorithms are versatile in solving a variety of optimisation problems in the mining industry, although their application to SBO remains relatively limited due to a lack of suitable platforms for presenting algorithms. 512
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Following here are additional summaries of research conducted in the field of optimising the extraction boundaries of underground mines: Jalali and Hosseini (2009) simulated a two-dimensional block model of the mineral using a graph and then employed Dijkstra’s greedy algorithm to find the path with the highest weight on the graph. The path determined by Dijkstra’s algorithm represents the optimal boundaries of underground mining stopes in twodimensional sections. This approach effectively illustrates how graph theory can be applied to optimise extraction strategies in underground mining operations. Nelis et al. (2016) proposed a mathematical model aimed at achieving geomechanical stability in designed stopes. Basiri and Pourrahimian (2018) developed a mathematical model for finding the optimal stope layout and production scheduling in the sublevel stoping method. This model focuses on optimising both the stope layout and production timing based on the entire block model as well as on individual levels of the block model, addressing the impact of level separation on layout and scheduling. Villalba Matamoros and Kumral (2019) proposed a three-stage stochastic optimisation model using GA to account for ore-grade uncertainty. The first stage assesses uncertainty in the stope layout, the second stage develops average designs and evaluates feasibility, creating the initial population for the GA, and the third stage uses GA to improve this population over successive generations. This approach yields higher profitability, reduced planned dilution, and more robust stopes less sensitive to grade fluctuations. Hou et al. (2019) examined the simultaneous optimisation of stope boundaries and the placement of openings, though they only considered shafts among the various types of primary openings. Mmola et al. (2019b) proposed a model for optimising stope boundaries but acknowledged that it cannot yield the true optimal solution. This highlights the challenges of balancing geological complexity and operational constraints in underground mining. Shami-Qalandari et al. (2022) employed the floating-stope algorithm to determine optimal stope boundaries in block caving, taking ore-grade uncertainty into account. Furtado e Faria et al. (2022) introduced a stochastic integer programming model incorporating ore-grade uncertainties and the costs of mining openings in stope-boundary design. Due to the high number of decision variables, this model faces significant computational time challenges, limiting its use in large-scale problems. Morales et al. (2022) proposed a heuristic algorithm for determining stope boundaries in the sublevel mining method by reducing the number of variables through eliminating geotechnically unstable stopes, thereby accelerating solving time. However, as a heuristic, it does not guarantee optimality. Mutandwa and Musingwini (2024) provided the latest review on optimal boundary determination methods for underground mining stopes, critically analysing topics, concepts, trends, and spatial information from prior work. A schematic of the sublevel stoping mining method is depicted in Figure 1. In the present study, the SBO problem for sublevel stoping mines is addressed using a hybrid network-flow–PSO algorithm. The proposed model conducts optimisation for each level, enhancing the accuracy of the optimisation process. In the sublevel mining method, the mining area is divided into levels of varying heights based on host rock mass quality and machinery specifications. Figure 2 illustrates the stope boundaries of an underground mine across different levels. The implementation of a hybrid algorithm in the proposed The Journal of the Southern African Institute of Mining and Metallurgy
Underground mine stope boundary optimisation using a hybrid network flow-particle Table 1 A summary of the recent hybrid algorithms used to solve underground mine design and planning problems Stope boundary optimisation Year
Author/s
Algorithm
Mining method
Mathematical formulation
Dimension
2019
Nikbin et al.
Dynamic Programming and Greedy Algorithm
All
Yes
3D
2020
Sari and Kumral
Dynamic Programming and Greedy Algorithm
Sublevel stoping
Yes
3D
2022
Nikbin et al.
Greedy algorithm and a pattern search technique
All
Yes
3D
2022
Nhleko and Musingwini
The DIA interchanges the particle swarm optimization algorithm and The genetic algorithm
All
No
3D
Production planning Year
Author
Algorithm
Objective function
Description
➤ Therefore, IMR is an innovative 2019
Mousave and Sellers
CM and IMR
Maximisation of NPV
and interesting approach, which can be employed to recover low grade ore. ➤ The value of the project is very sensitive to the assumed metal recovery.
➤ Application of no dominated sorting 2019
Foroughi et al.
NSGII and MIOP
Figure 1—A schematic of sublevel mining method (Sotoudeh et al., 2020) The Journal of the Southern African Institute of Mining and Metallurgy
Maximisation of NPV
genetic algorithm. ➤ Integration of stope layout and production scheduling. ➤ Reduction so solution time significantly (from 7-8 h to 7 days) for a complex problem.
Figure 2—Example of three levels in the sublevel stoping method VOLUME 126
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Underground mine stope boundary optimisation using a hybrid network flow-particle model also addresses the problem of raise location optimisation. Unlike the model proposed by Bai et al. (2013), which overlooks geotechnical constraints, the present model prioritises these constraints as crucial factors. By considering geotechnical constraints, the optimised stopes exhibit varied dimensions, preventing the mining of economically unjustifiable sections. The proposed model comprises two primary components: ➤ Utilising the PSO algorithm, the model identifies raise locations for each stope, maximising economic value. ➤ The NFA determines the range of blocks surrounding the raises, while accounting for existing constraints. The proposed model is implemented on an economic block model, excluding blocks with negative economic value for the selection of the raise’s location (Figure 3). While it is possible that blocks with negative economic value may fall within the stope boundaries, the aim is to ensure that these blocks have the minimal possible negative value. The method for selecting the stope sequencing in this study is based on the selection of a “raise” location. The search does not initiate from the position of the first block; instead, a random location (coordinates x, y, z) is selected, which serves as the raise’s position. Subsequently, all blocks that fall within a minimum width range are identified. After that, the blocks are evaluated using the MRMR classification system to determine the maximum stable width. The mining of these blocks is then carried out according to the precedences outlined in the
Figure 3—A 2D Example of stope sequencing in present model
manuscript. Finally, the stope boundaries are established. This process continues until the entire block model has been searched using the algorithm, taking into account the constraints of block and stope overlap. Below is an example of stope sequencing. ➤ Represent a 2D block model ➤ Randomly selected the Raise location ➤ Determine blocks located in minimum width ➤ Determine maximum width and its blocks (note negative blocks doesn’t selected) ➤ Selected the second raise location for determine boundary of second stope ➤ Represent the blocks in minimum width ➤ Shown maximum width and its blocks *The overlapping constraint is considered in this method.
Methodology Network flow algorithm In solving SBO problems, the use of algorithms with a reasonable solution speed is important. The NFA stands out for its ability to maintain low solution times, even as the model size increases, as noted by Bai et al. (2013). The NFA uses the concept of graph theory, initially introduced by Johnson (1968), for determining the ultimate pit limit (UPL). Picard (1976) introduced a new interpretation of the network flow algorithm, shifting the focus from maximum flow to minimum closure. In Picard’s revised network structure, source (s) and sink (t) nodes are incorporated at the start and end, respectively, facilitating efficient problem resolution. In the weighted graph G, V represents the weighted blocks, while E represents the relationship between the blocks. In Picard's (1976) revised graph, add to source (so) and sink (si) node to start and end of graph and blocks that have positive economic value are linked to the (so), while blocks with negative economic value (adjusted to their absolute value) are connected to the (si). Solving the network-flow problem entails employing algorithms designed for solving the maximum-closure problem. Examples include the Fork-Fulkerson algorithm used by Nabi et al. (2017) and the push-relabel algorithm utilised by Bai et al. (2013). In the present study, the push-relabel algorithm, recognised
Figure 4—A 2D schematic graph of network flow in open-pit mine 514
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Underground mine stope boundary optimisation using a hybrid network flow-particle as one of the common and capable algorithms for solving the maximum flow problem, is employed. Figure 3 shows the block relationships in an open pit mine with a 45-degree slope. Figure 4 (a – c) depict a 2D block model along with surface mining constraints for each block. Figure 5 (a) showcases a 2D overlapping of stopes denoted by Bij positioned between every two stopes, while Figure 5 (b) illustrates three non-overlapping stopes. Figure 6 (a – d) present the primary blocks for each block in the sub-surface mining method. Figure 6 (a, b) show the surrounding blocks, Figure 6 (c) showcases the surrounding blocks behind, and Figure 6 (d) illustrates the blocks in front of the desired block. One advantage of the NFA is its capability for optimising stope boundary in three dimensions while adhering to mathematical logic, enabling the attainment of true optimal solutions. The constraints employed in this study to optimise the stope boundary include: ➤ Maximum and minimum stope width
Figure 5—A 2D graphical model of the overlap blocks, a) Bij is the overlapping block in two stopes, b) stopes with the constraint of overlapping
In underground mines, a minimum width is considered for each stope based on the ’physical and mechanical’ properties of the host rock. This minimum width ensures adequate space for blasted rock material to settle on the stope floor. Additionally, each stope has a maximum width determined by technical, economic, and geotechnical considerations. The selection of the maximum width aims to ensure stope stability while maximising the objective function. ➤ Maximum and minimum working height Minimum height requirements in underground mine levels are controlled by the machinery used for drilling, loading, and hauling operations. Determining maximum height involves considering technical and economic factors, such as geological conditions, hydrogeology, lithology, host rock properties, and ore body depth as noted by Jakubec and Laubscher (2000). Heights may vary across levels. ➤ Overlapping stopes Addressing stope boundary optimisation necessitates considering stope overlap, wherein part of one stope encroaches into another (Sandanayake et al., 2015). As shown in Figure 4, overlapping occurs when multiple high-grade blocks are shared among two or more stopes, potentially leading to negative economic value for all affected stopes. The floating stope algorithm fails to account for stope overlap and may struggle to find satisfactory solutions for large-scale problems. ➤ Geotechnical conditions Geotechnical considerations play a crucial role in stope boundary optimisation. These conditions control the maximal stope span for stability and maximal height. The proposed algorithm integrates the MRMR classification system, recommended by Esmaeili et al. (2023), to identify stable stopes based on geotechnical criteria.
Figure 6—A 3D precedence block (Xc) in underground mining method The Journal of the Southern African Institute of Mining and Metallurgy
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Underground mine stope boundary optimisation using a hybrid network flow-particle PSO algorithm The PSO algorithm stands out as one of the most efficient optimisation algorithms, praised by designers for its simplicity and minimal parameter requirements, as noted by Wang et al. (2018). The PSO algorithm begins with the random generation of an initial population, where each member is characterised by velocity and position vectors within the search space. Each particle evaluates the target function as its current position, then determines a direction to move, based on its own best position (pbest) and the best position found by the swarm (gbest). This process repeats iteratively until the desired solution is achieved. Figure 7 illustrates the optimisation process facilitated by the PSO algorithm. Key parameters of the PSO algorithm include inertia weight and its adjustment rate, personal learning coefficient, and collective learning coefficient. While the PSO algorithm has found application in various optimisation problems within the mining context, such as blasting optimisation by Zhang et al. (2020), production planning in open pit mining by Khan and Niemann Delius, (2014a: 2014b), Click or tap here to enter text., and underground mining by Zhao et al. (2022), its utilisation in SBO remains relatively limited. Additionally, PSO has been employed for identifying unstable zones, as demonstrated by Mahdevari and Khodabakhshi (2021). In the present study, the PSO algorithm is employed to optimise the location of raises, with the particle size set equal to the number of initial solutions. Fifty initial solutions are generated for the model, which serve as the number of particles in the algorithm. Each particle’s initial location corresponds to one of the initial solutions, with the objective of improving these solutions iteratively through updates to particle velocity; position, and the inertia weight (ω) selected in Table 2. The optimal particle location corresponds to the central raise location in each stope, a critical factor influencing project profitability. Figure 8 provides a graphical depiction of the
Figure 7—A graphical PSO algorithm
PSO algorithm and its associated variables. Raises are pivotal in the proposed model, as determining their locations is essential for stope delineation. Figure 9 outlines the flowchart of the proposed hybrid algorithm. [1] [2] [3] In Equation 1, wi represents the coefficient of inertia, where i is the maximum iteration. In Equation 2, r1 and r2 are two random vectors with values between 0 and 1. c1 and c2 are the learning parameters, typically set to c1 = c2 = 2 yielding the new velocity. Equation 3 determined the new position of particle.
Mathematical modelling
Table List of selected parameters in PSO
516
Parameters
Symbol
Number
First random numeric
r1
Random numeric between (0, 1)
Second random numeric
r2
Random numeric between (0, 1)
First speed factor
C1
2
Second speed factor
C2
2
Number of particles
Particle size
50
Maximum speed
Vmax
1
Minimum speed
Vmin
-1
Maximum coefficient of inertia
ωmax
0.9
Maximum coefficient of inertia
ωmin
0.4
Number of iterations
Iteration
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Underground mine stope boundary optimisation using a hybrid network flow-particle Table 3 Subscript notations, sets, parameters, and decision variables used in the hybrid model Parameters
ϑi Economic value (USD) C p Processing cost (USD/tonne) P Price (UDS/tonne)
Cm
Mining cost (UDS/tonne)
γ i Block density i (t/m3) Figure 8—PSO algorithm flowchart
R
Recovery (%)
Vi
Mineral volume (m3)
gi
Grade block i (%)
T
Tonnage (tonne)
I
The number of blocks located in the x-axis, i ∈ {1,...., I }
J The number of blocks located in the y-axis, j ∈{1,..., J } K The number of blocks located in the z-axis, k ∈{1,..., K } o Block index Decision variables
c ∈ ξi '
If block b is extracted, it is equal to one, otherwise it is zero.
Indices and sets
c ∈ ξi ' Figure 9—Flowchart of hybrid algorithm
Figure 10—Central raise in the stope and the surrounding blocks (Xc) The Journal of the Southern African Institute of Mining and Metallurgy
ξi '
Precedence block for block i.
A set of blocks that must be mined before the block i.
A hybrid model is proposed to incorporate all inherent constraints of the sublevel mining method while optimising the stope boundary. The model is centred around central raises; pivotal in the optimisation process. The blocks surrounding each raise, forming mining stopes, are shown in Figure 10. In the present study, two algorithms are employed to optimise the stope boundary. The first algorithm utilises the constraints outlined in the aforementioned to determine the initial raise location and subsequently locates subsequent raises based on this initial position. This is achieved using a PSO algorithm. Next, incorporating the inherent constraints of the sublevel mining method and considering geotechnical constraints to determine the VOLUME 126
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Underground mine stope boundary optimisation using a hybrid network flow-particle maximal stable span for every stope, the stope boundary for each level is obtained using the network flow algorithm. Formulating concise constraints is a key strategy to reduce solution time in optimisation problems. Table 3 presents the symbols, sets, parameters, and decision variables used in the model. [4] [5] The hybrid model developed is as follows:
Objective function: [6] [7]
Subject to: [8] [9] [10] [11] [12] [13] [14] [15] [16] Equation (4) is employed to determine the tonnage of a block i, where V denotes the number of nodes, x b represents the desired block, and x c signifies a set of precedence blocks for each block. Equation (5) converts the grade block model to the economic block model. Equations (8 – 16) determine all precedence blocks surrounding each raise. Note again that the primary objective of this research is to maximise economic value. This objective is followed by sub-goals such as maximising the recovery of mined blocks, as depicted in Equation 17. [17]
Case study The proposed algorithm was applied to a case study of an underground copper mine in Chile, with the deposit located 1,000 m below ground level. The study used data for 4,100 blocks, each measuring 10 m × 10 m × 10 m, corresponding to a single mine level (Figure 11). The economic parameters are listed in Table 4. After the PSO algorithm determined the location of the raises, the network flow was used to delineate the stope boundary. The results from the model are presented in Table 5. For validation, these results were compared with those obtained from the network-flow, hybrid, and MVN algorithms. Notably, the MVN algorithm, a commercial method that accounts for the overlapping 518
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Table 4 List of economic values for the copper reserve Parameter
Value
Unit
Price of copper
6000
UDS/tonne
Mining costs
8
USD/tonne
Processing costs
30
USD/tonne
Recovery
90
%
constraint and is applicable to all underground mining methods, performed satisfactorily, yielding a 4% increase in network flow and approximately a 7.5% improvement in MVN. Consequently, it was incorporated into the new model. An important issue in underground mining is preparation costs. In the sublevel stoping mining method, the height of the sublevels and the size of the stopes play a crucial role in reducing these costs. Optimising these parameters can lead to substantial reductions in drilling, blasting, and other preparatory activities associated with mining operations. As shown in Figure 12, more than 30% of development and backfilling costs in underground mines employing sublevel stoping are significant. Consequently, increasing the number of stopes raises development costs. In addition, a larger number of stopes also increases drilling and blasting costs because more holes must be drilled and blasted to create the initial space for extraction. Therefore, having fewer stopes can lower development costs, which can be achieved by enlarging stope dimensions. However, larger stope dimensions do not automatically translate into greater economic value. Figure 13 shows that there is an economically optimal stope size; beyond this point, larger sizes have a negative impact. The aim of this study is to determine the extraction stope boundaries with the maximum economically optimal size that yields the highest economic value and stability. In the case study, all stope heights were set at 40 metres, as determined by the MRMR classification system. The levels and stope heights are assumed to be equal; by increasing the stope height and reducing the number of levels, development costs can be lowered. In sublevel stoping and other underground mining methods, the majority of production costs are incurred during stoping, contributing significantly to the total cost of ore production. Therefore, it is crucial to estimate the cost per tonne of ore mined; this can be misleading if the cost per unit of metal recovered is overlooked. Dilution, which arises during loading, hauling, hoisting, and processing, can affect the cost per unit of metal produced. A strategy that minimises cost per tonne may, in some cases, lead to ore losses or excessive dilution with waste, resulting in suboptimal economics for a given scenario. The optimal approach should balance ore recovery, dilution, and unit costs to identify the most economical solution for the deposit. Figure 12 shows direct stoping costs for several underground mining cases using open stoping, shrinkage stoping, and cut-and-fill (C&F) stoping. These costs are expressed on a per-tonne basis and illustrate the effect of stope span (in feet) on direct stoping cost per tonne of ore. Higher costs in narrow stopes arise from lower tonnes broken per foot, drilled and reduced output per man-shift. The curves also highlight other cost factors. In open stoping, operating costs are particularly high, as indicated by region A in Figure 12, due to ore-body irregularity, frequent displacements of the ore body, and mining of thin ore near the margins. A higher-than-usual cost occurs when stopes are about 25 feet wide, but much of the ore is The Journal of the Southern African Institute of Mining and Metallurgy
Underground mine stope boundary optimisation using a hybrid network flow-particle
Figure 11—Block model of the under-study copper ore deposit
Figure 11—A summary of the costs associated with mining for ordinary sublevel open stoping operations (Adler, Thompson, 2011)
Figure 12—Curves indicating effect of stoping widths on direct cost (Adler, Thompson, 2011)
very thin, as shown at B. Costs for C&F stoping are higher than normal at points labeled SS, T, and T. The two T labels illustrate underground mines employing a stringer‑and‑fill method with substantial timbering under very heavy hanging-wall conditions, requiring cautious extraction. The higher cost is associated with substantial square‑set stoping relative to C&F stoping, as shown at SS. Hence, stope width affects costs. It is worth noting that very large stope spans are not always advantageous, as Figure 13 shows. It is important to design stopes with spans that provide both stability The Journal of the Southern African Institute of Mining and Metallurgy
and maximum economic value. Because the model uses an MRMR classification system, it can identify stopes with the maximum stability span. As Figure 13 indicates, if the stope size exceeds the optimum, external dilution may increase; if it is smaller than the optimum, the maximum economic value is not obtained. By running the model on an economic block model, mineable stopes were identified that exhibit high stability and economic value. Table 4 presents the parameters of economic value of copper reserve, while Table 5 details the number of mineable stopes and the VOLUME 126
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Underground mine stope boundary optimisation using a hybrid network flow-particle value. Incorporating a geotechnical constraint into the model allows production stopes to be developed up to their maximum stable dimensions. As stope dimensions increase, the number of raises decreases, leading to reduced operational costs. This constraint yields stopes with varying dimensions, a feature not commonly addressed in existing models.
Results and discussion In this study, the primary objective was to maximise economic value while solving the SBO problem within a reasonable timeframe. The optimum location obtained from the hybrid algorithm is presented in Table 5. After 100 iterations, an economic value of USD211,167,572 was achieved, accompanied by an ore recovery of 93.58%, as shown in Figure 14. However, of the 18 stopes created, only 13 were deemed mineable. While the networkflow algorithm generated an economic value of USD202,720,870, representing a 4% decrease compared to the hybrid algorithm, the MVN algorithm yielded USD196,385,832, a decrease of 7.5%. Utilising the networkflow algorithm for stope-boundary optimisation, complemented by the PSO heuristic for raise-location optimisation, the proposed model successfully approximates the optimal solution. The resulting stope layout, visualised in 3D in Figure 16, shows stopes with maximum economic value. Figure 17 illustrates the locations of raises and mineable stopes. Each black dot denotes a raise location on a level. The stopes surrounding these raises vary in size, yielding a diverse range of stope dimensions. For clarity, Figure 18(a) presents all unmined and mineable stopes with different average grades, while Figure 18(b) highlights mineable and unmined stopes. Additionally, applying the model to a copper deposit comprising
Figure 13—Relation between stope value and stope size
Figure 14—Result of the hybrid algorithm after 100 iterations
Figure 15—Results of PSO-Max-flow and Max-flow
economically unjustified stopes left unmined. The PSO algorithm played a crucial role in identifying optimal raise locations. Reducing the number of raises can potentially lower costs associated with maximising both stope stability and economic
Figure 16—Mineable stope from the hybrid algorithm in 3D
Table 5 Results obtained from running the hybrid algorithm on the case study Objective functions
Number of stopes
Location of initial raise
Recovery (%)
Economic Value ($)
Mined Stopes
Unmined Stopes
Total
X-direction
Y-direction
93.58
211,167,572
13
5
18
13821
25020
520
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Figure 17—Created stopes and location of raises from the hybrid algorithm in 2D view
Figure 18—Visualisation of the average grade of each stope in 3D: (a) stope boundaries, (b) minable stope
56,953 blocks across three levels demonstrated its scalability and efficacy. Figure 19 provides an overview of the economic value achieved at each level after 100 iterations. Notably, Table 6 compares the solution times of the networkflow and the hybrid algorithm; the networkflow algorithm exhibits significantly shorter solution times, while the hybrid method’s solution time is influenced by the number of stopes generated. In open stoping methods, minimising the number of stopes can enhance profitability due to the high costs involved. Across the first, second, and third levels, the algorithm produced 44, 40, and 36 stopes, respectively, of which only a subset was deemed mineable. Table 7 provides details on the locations of initial raises across the three levels of the larger case study. Figure 20 presents the stope
Figure 19—Result of the hybrid algorithm after 100 iterations for three levels
Table 6 Results obtained from running the hybrid algorithm on the case study Levels
Number of stopes
Economic value (USDmillion)
Mined stopes
Network flow solution time (S)
Hybrid solution time (h: m: s)
Level-1
44
8,091
36
0.17
0:17:11
Level-2
40
3,843
33
0.40
0:15:36
Level-3
36
3,587
28
0.13
0:11:23
Table 7 Location of initial raise in three levels in a bigger case study Levels
Location of initial raise X
Y
Level-1
13971.44
24951.90
Level-2
14242.11
25328.54
Level-3
14037.87
25221.65
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layout and highlights the impact of geotechnical constraints on stope dimensions, with each stope colour-coded to represent its average grade.
Conclusion
Determining an optimal stope boundary is a critical aspect of underground mine design and planning, directly affecting project profitability. However, the complex constraints inherent to underground mining pose substantial challenges to developing efficient optimisation algorithms. Among these constraints, VOLUME 126
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Underground mine stope boundary optimisation using a hybrid network flow-particle Ataee-Pour, M, 2004. Optimisation of stope limits using a heuristic approach. Mining Technology vol. 113, pp. 123–128. Ataee-Pour, M. 2000. A heuristic algorithm to optimise stope boundaries. Bai, X., Marcotte, D., Simon, R. 2013. Underground stope optimization with network flow method. Computers and Geoscience 52, pp. 361–371. https://doi.org/10.1016/J. CAGEO.2012.10.019 Basiri, Z., and Pourrahimian, Y. (2018). “Application of mathematical programming for stope layout and production schedule optimization in sublevel stoping”. Mining Optimization Laboratory, vol. 8, no. 1, pp. 235–245. Copland, T., Nehring, M. 2016. Integrated optimization of stope boundary selection and scheduling for sublevel stoping operations. Journal of the Southern African Institute of Mining and Metallurgy. vol. 116, pp. 1135–1142. Esmaeili, A., Hamidi, J.K., Mousavi, A. 2023. Determination of sublevel stoping layout using a network flow algorithm and the MRMR classification system. Resources Policy 80, pp. 103265.
Figure 20—Visualisation of the average grade of each stope in 3D
geotechnical conditions play a pivotal role in ensuring stope stability. This study presents a hybrid model to address the SBO problem within the sublevel stoping method, accommodating each level of the mine. Central raises within each stope form the basis of the proposed model and significantly influence its optimisation. The PSO algorithm is used to optimise the location of these raises, yielding near-optimal solutions. Comparative analysis shows that the hybrid model outperforms both the MVN and network-flow algorithms in terms of economic value, achieving approximately 4% and 7.5% higher economic values, respectively. Notably, the hybrid model offers versatility, capable of optimising both regular and irregular block models in 3D space. Although PSO is heuristic, it demonstrates remarkable efficiency by delivering fast, feasible solutions close to optimality. While the network flow providing a solid mathematical basis for potentially true optimal be limited in practice by its computational complexity, the hybrid model presented here offers a promising solution to the challenging problem of stope boundary optimisation in underground mining, illustrating the benefits of integrating heuristic methods with mathematical optimisation models.
CRediT authorship contribution statement Ahmadreza Esmaeili: Methodology, software, visualisation, investigation, writing – original draft. Jafar Khademi Hamidi: Writing – review and editing, conceptualisation. Amin Mousavi: Writing – review and editing, conceptualisation
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Furtado e Faria, M., Dimitrakopoulos, R., Pinto, C. 2022. Stochastic stope design optimisation under grade uncertainty and dynamic development costs. International Journal of Mining, Reclamation and Environment. vol. 36, pp. 81–103. Hou, J., Xu, C., Dowd, P.A., Li, G. 2019. Integrated optimisation of stope boundary and access layout for underground mining operations. Mining Technology 128, pp. 193–205. Jakubec, J., Laubscher, D.H. 2000. The MRMR rock mass rating classification system in mining practice, in: Proceedings of the 3rd International Conference and Exhibition on Mass Mining, Brisbane, Australia. pp. 413–421. Jalali, S.E., Ataee-Pour, M. 2004. A 2D dynamic programming algorithm to optimize stope boundaries, in: Proceedings of the 13th Symposium on Mine Planning and Equipment Selection, Rotterdam, Balkema. pp. 45–52. Jalali, S.E., Hosseini, H. 2009. Optimization of Extraction Range in Underground Mining Using a Greedy Algorithm. Journal of Science and Research in Mining Engineering 4, pp. 1–11. Johnson, T.B. 1968. Optimum open pit mine production scheduling. University of California, Berkeley. Khan, A., Niemann-Delius, C. 2014a. Production scheduling of open pit mines using particle swarm optimization algorithm. Advances in Operations Research 2014. Khan, A., Niemann-Delius, C. 2014b. Application of particle swarm optimization to the open pit mine scheduling problem, in: Proceedings of the 12th International Symposium Continuous Surface Mining-Aachen 2014. Springer, pp. 195–212. Lamghari, A., Dimitrakopoulos, R., Ferland, J.A. 2015. A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines. Journal of Global Optimization 63, pp. 555–582. Mahdevari, S., Khodabakhshi, M.B. 2021. A hybrid PSO-ANFIS model for predicting unstable zones in underground roadways. Tunnelling and Underground Space Technology 117, pp. 104167.
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Underground mine stope boundary optimisation using a hybrid network flow-particle Matamoros, M.E.V., Kumral, M. 2017. Heuristic stope layout optimisation accounting for variable stope dimensions and dilution management. International Journal of Mining and Mineral Engineering. vol. 8, pp. 1–18. Mmola, T.M., Nhleko, A.S., Atherfold, J.M. 2019a. Application of Particle Swarm Optimization Algorithm to Optimize Stope Layout for Underground Mines, in: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (Eds.), Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection - MPES 2018. Springer International Publishing, Cham, pp. 213–221. Mmola, T.M., Nhleko, A.S., Atherfold, J.M. 2019b. Application of particle swarm optimization algorithm to optimize stope layout for underground mines, in: Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection-MPES 2018. Springer, pp. 213–221. Morales, N., Mancilla, D., Miranda, R., Vallejos, J. 2022. A fast method to develop an optimal operational sublevel stope design. Resources Policy 77, pp. 102670. Mutandwa, B., Musingwini, C. 2024. Insights and future research directions from a bibliometric mapping of studies in stope layout optimisation. International Journal of Mining, Reclamation and Environment. vol. 38, pp. 577–595. Nabi Rebaz M., Azad, R., Saeed, S., Nabi Rebwar, M. 2017. Optimization Algorithm’s Problems: Comparison Study. Kurdistan Journal of Applied Research 2, pp. 25–31. Nelis, G., Gamache, M., Marcotte, D., Bai, X. 2016. Stope optimization with vertical convexity constraints. Optimization and Engineering 17, pp. 813–832. Nhleko, A.S., Musingwini, C. 2022. Optimisation of ThreeDimensional Stope Layouts Using a Dual Interchange Algorithm for Improved Value Creation. Minerals 12, vol. 501. Nhleko, A.S., Tholana, T., Neingo, P.N. 2018. A review of underground stope boundary optimization algorithms. Resources Policy 56, pp. 59–69. Nikbin, V., Ataee-pour, M., Shahriar, K., Pourrahimian, Y. 2020. A 3D approximate hybrid algorithm for stope boundary optimization. Computers and Operations Research 115, vol. 104475. https://doi.org/10.1016/j.cor.2018.05.012 Nikbin, V., Ataee-pour, M., Shahriar, K., Pourrahimian, Y., MirHassani, S.A. 2019. Stope boundary optimization: A mathematical model and efficient heuristics. Resources Policy 62, pp. 515–526. Ovanic, J. 1998. Economic optimization of stope geometry. Michigan Technological University. Picard, J.-C. 1976. Maximal closure of a graph and applications to combinatorial problems. Management science. 22 , pp. 1268– 1272. Riddle, J.M. 1977. A dynamic programming solution of a blockcaving mine layout, in: Proceedings The 14th APCOM
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Symposium, Society of Mining Engineers-American Institute of Mining, Metallurgy, and Petroleum Engineers, New York. pp. 767–780. Sandanayake, D.S.S., Topal, E., Ali Asad, M.W. 2015. A heuristic approach to optimal design of an underground mine stope layout. Applied Soft Computing. vol 30, pp. 595–603. https://doi.org/10.1016/J.ASOC.2015.01.060 Sari, Y.A., Kumral, M. 2020. A planning approach for polymetallic mines using a sublevel stoping technique with pillars and ultimate stope limits. Engineering Optimization 52, pp. 932–944. Shami-Qalandari, M., Rahmanpour, M., Mirabedi, S.M. 2022. Determining a resilient stope boundary for underground mass mining projects. Rudarsko-geološko-naftni zbornik 37, pp. 103–116. Sotoudeh, F., Nehring, M., Kizil, M., Knights, P., Mousavi, A. 2020. Production scheduling optimisation for sublevel stoping mines using mathematical programming: A review of literature and future directions. Resources Policy 68, pp. 101809. Tolouei, K., Moosavi, E., Tabrizi, A.H.B., Afzal, P., Bazzazi, A.A. 2020. Improving performance of open-pit mine production scheduling problem under grade uncertainty by hybrid algorithms. Journal of Central South University 27, pp. 2479– 2493. Topal, E., Sens, J. 2010. A new algorithm for stope boundary optimization. Journal of Coal Science and Engineering (China) 16, pp. 113–119. https://doi.org/10.1007/s12404-010-0201-y Villaescusa, E. 2014. Geotechnical design for sublevel open stoping. CRC Press. Villalba Matamoros, M.E., Kumral, M. 2019. Underground mine planning: stope layout optimisation under grade uncertainty using genetic algorithms. International Journal of Mining, Reclamation and Environment. vol. 33, pp. 353–370. Wang, D., Tan, D., Liu, L. 2018. Particle swarm optimization algorithm: an overview. Soft computing 22, pp. 387–408. https://doi.org/10.1007/s00500-016-2474-6 Zaidan, A.A., Atiya, B., Abu Bakar, M.R., Zaidan, B.B. 2019. A new hybrid algorithm of simulated annealing and simplex downhill for solving multiple-objective aggregate production planning on fuzzy environment. Neural Computing and Applications 31, pp. 1823–1834. https://doi.org/10.1007/s00521-017-3159-5 Zhang, X., Nguyen, H., Bui, X.-N., Tran, Q.-H., Nguyen, D.-A., Bui, D.T., Moayedi, H. 2020. Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost. Natural Resources Research 29, pp. 711–721. https://doi.org/10.1007/s11053-019-09492-7 Zhao, Y., Chen, J., Yang, S., Chen, Y. 2022. Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm. Mathematics 10, pp. 2418. https://doi.org/10.3390/math10142418 u
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YPC EVENTS Underground mine stope boundary optimisation using a hybrid network flow-particle SERIES 2026 YPC EVENTS SERIES 2026 The role of youth and young professionals in Future Proofing African Today The roleMining, of youth and young professionals in Future Proofing African Mining, Today BACKGROUND AROUND THE THEME • Focus on sustainability and innovation
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Integrating Bayesian, ensemble, and hybrid machine learning models for predicting overbreak and underbreak in underground stope mining by S.Taheri1, E.Topal1, R.S. Faradonbeh1, H.Jang2 Affiliation: 1 Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, WA, Australia 2 Hanwha Mining Services Australia, Leeming, WA, 6149, Australia Correspondence to: S. Taheri Email: sina.taheri@me.com
Dates:
Received: 05 Jun. 2025 Revised: 23 Oct. 2025 Accepted: 21 Jan. 2026 Published: Sep. 2026 How to cite: Taheri S., Topal E., Faradonbeh R.S., Jang, H. 2026. Integrating Bayesian, ensemble, and hybrid machine learning models for predicting overbreak and underbreak in underground stope mining. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 9, pp. 525 – 540 DOI ID: https://doi.org/10.17159/2411-9717/3746/2026 ORCiD: S.Taheri https://orcid.org/0009-0009-7229-6414 E.Topal https://orcid.org/0000-0002-9564-3484 R.S. Faradonbeh https://orcid.org/0000-0002-1518-3597 H.Jang https://orcid.org/0000-0002-3978-5840
Abstract
Overbreak and underbreak in underground stoping operations cause significant challenges, impacting safety and operational efficiency, leading to dilution and ore loss. This study employs machine learning methods to predict overbreak and underbreak at individual drillhole locations, enabling dynamic prediction of stope shapes before blasting in underground mining. Based on a comprehensive database collected from a longhole sublevel stoping mine in Western Australia, different machine learning-based models, including multiple linear regression, random forest regression, gradient boosting machine, and Gaussian process regression, were developed systematically. The best-performing model was the initial Gaussian process regression with the Matérn 3/2 kernel, achieving R² value of 0.820 and a root mean squared error value of 0.452. A series of hybrid models were also developed to improve prediction accuracy by combining Gaussian process regression, gradient boosting machine[A5.1], random forest regression, and artificial neural network with linear meta-learners. The best hybrid configuration achieved with R² value of 0.721 and RMSE value of 0.425, but further complexity led to overfitting with no gains in generalisation. SHapley Additive exPlanations analysis across all models highlighted the Mathews stability number, ratio of burden to spacing, and void ratio as the most influential variables, varying in impact between the random forest regression, gradient boosting machine, and Gaussian process regression models.
Keywords
Overbreak, underbreak, random forest, Gaussian process, gradient boosting machine, hybrid models
Introduction Underground mining operations face significant challenges in managing overbreak (OB) and underbreak (UB), which can substantially affect operational costs, safety, and overall efficiency, from extraction through to processing in mine-to-mill workflows. Accurate prediction of these events is essential for optimising mine design, execution processes, and costs. Over the years, the majority of researchers have analysed machine learning applications in surface mining, as reviewed by McCoy (2019), however, there has been limited analysis into more complex machine learning techniques and their application in underground mining. Additionally, other underground-related studies focus on general underground issues, such as underground rock bursts and OB and UB studies in tunnelling and development, rather than specifically addressing OB and UB in underground stoping operations. Some approaches, such as those using point cloud data, oversimplify the models, potentially overlooking the complexities of underground drilling and blasting dynamics. Jung and Choi (2021) reviewed 109 papers that revealed active research in machine learning (ML) within the mining industry, particularly since 2018, with a focus on mineral exploration. While empirical methods and regression models have traditionally been employed, the inherent complexities of geological conditions and operational variables demand more sophisticated analytical approaches. Sellers and Salmi (2020) analysed blasting in various underground operations, highlighting the associated challenges. The study illustrates how the lack of continuous, accurate 3D measurements of crucial properties like blastability, in situ structures, drillhole deviation, and fragmentation, along with a limited understanding of mining-induced stresses, frequently lead to issues such as overbreak, dilution, production delays, reduced excavation stability, and poor plant performance. Their study highlights the necessity for a data-driven approach to effectively monitor and predict important factors in the dynamic mining environment. In a recent study, Abd Elwahab et al. (2023) reviewed the application of ML methods in mine blasting, noting that hybrid and ensemble models outperformed other approaches in 61% of case studies. The study highlighted trends in ML adoption, particularly for surface blast issues, where hybrid models were applied in 72% of cases,
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Integrating Bayesian, ensemble, and hybrid machine learning models compared to 45% for underground blasting. The review emphasised the growing role of ML in optimising blasting processes and decision-making while highlighting the need for further research, especially in underground blasting operations. Additionally, studies involving artificial neural network (ANN)-based classifiers suffer from limited validation on larger datasets, making it difficult to assess their broader applicability. While methods like histogram gradient boosting trees (HGBT) and Bayesian model averaging show potential, they often lack comparison with other models and fail to incorporate more complex geological and geotechnical parameters. Previous researchers have faced other challenges in their studies, which involved limited access to the broad drill and blast datasets; as Suglo (2012) discussed, the challenges of assessing dilution and ore loss in sublevel caving due to poor data quality. A considerable body of research has focused on OB, UB, dilution, and ore loss in underground mining. These issues significantly impact mine economics, safety, and the downstream processing efficiency. Various predictive models and analytical frameworks have been developed, ranging from statistical and empirical methods to advanced machine learning and simulation techniques. For instance, studies by Liu et al. (2023) applied ensemble learning for rockburst prediction in relation to geological conditions, while Zhao and Jia'an (2020) employed neural networks to model unplanned dilution using overbreak-based indicators. Others, such as Papaioanou and Suorineni (2016), developed stability graphs tailored to stope dilution prediction using extensive case study data. Despite these advances, limitations remain, ranging from reliance on simplified models or site-specific data to lack of validation and limited generalisability. The growing complexity of underground mining environments and data availability continues to drive the need for robust, adaptable predictive tools. Table 1 summarises key studies focused on OB and UB in underground mines, highlighting the parameters considered, methods applied, results obtained, and limitations of each approach. The predictors used in this study (see Table 2) represent both geotechnical and drill-and-blast design factors that collectively influence OB and UB. Parameters such as Mathews stability number (N') and hydraulic radius (HR) describe rock-mass stability and hanging-wall geometry, while the burden-to-spacing ratio (BS), powder factor (PF), and primer location (PRIM) drive explosive energy distribution and confinement. The void ratio (VR), defined as the available void space ahead of the blast directly affects the extent of break (Papaioanou, Suorineni, 2016). These predictors capture the dominant physical and design mechanisms responsible for deviations from the planned excavation boundaries. The approach in this study marks a shift from the traditional reliance on 2D OB and UB prediction, the limitations of applying models in longhole stoping with small datasets, and the restricted use of features in previous studies, most of which focused only on geotechnical parameters and a few drill and blast features, without considering the full range of factors influencing OB and UB. Some of the earlier studies also relied on simpler methods, which, while foundational in mining predictive modelling, struggle with capturing the nonlinear and complex nature of geological data and the variables affecting OB and UB. This study builds on these limitations by adopting a unique hole-by-hole prediction approach, where all 15 features were collected and analysed at the level of individual drillholes. This allows for the first time a more detailed and localised prediction of OB and UB at each drillhole location, rather than generalised stope-level estimations. All 526
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drillholes referenced in this study are production blastholes used in longhole stoping. The dataset comprises operational drilling and blasting, geotechnical, and geological parameters recorded at the drillhole level. Despite the growing interest in advanced predictive techniques, their practical application in predicting OB and UB in underground production mining remains limited. This study addresses these gaps by systematically assessing the performance of various predictive models to tackle dilution and ore loss challenges in underground mining. It advances existing work by applying RFR, GBM, GPR, and hybrid models with hyperparameter optimisation and sensitivity analysis to better understand the influence of different predictors on OB and UB outcomes. Importantly, it further extends the analysis through SHAP-based interpretation, providing a detailed view of how geological, drilling, blasting, and design variables influence the output, marking a novel shift toward holeby-hole OB and UB prediction in underground stoping operations. The findings and methodologies presented in this paper highlight the potential of integrating ensemble, Bayesian, and hybrid techniques to improve prediction accuracy in underground stoping, leading to safer and more cost-effective mining practices.
Site description and data collection process The study was conducted at an underground gold mining operation located to the north of Kalgoorlie, Western Australia, and has been operational for many years. This site utilises various extraction methods, notably the longhole sublevel stoping supplemented by paste-filling. In this research, an individual analysis of each drillhole was carried out to collect essential data for predicting OB and UB at specific drillhole locations, a critical component setting the foundation for developing a dynamic 3D predictive model. In this study, the term “3D prediction” refers to generating OB/UB predictions at every measured point along each drillhole. Because the drillholes define the perimeter of the stope envelope, the pointwise predictions of rings collectively form the three-dimensional representation of the final excavation boundary. The model, therefore, predicts the 3D deviation surface by first predicting each hole individually, which is the fundamental unit of spatial reconstruction in underground stoping. Additionally, the research included an examination of the spatial relationships between adjacent drillholes and their collective setup within the stope's ring and panels. Data were precisely collected through analysis of individual drillholes, the examination of reconciliation reports, a thorough review of existing drill and blast designs, and operational planning parameters at the mine site. In total, 193 distinct production drillholes from different zones of the mine were analysed, each containing 15 predictor variables and one output variable (OB/UB), resulting in 3,088 data entries used for model development and evaluation. Initially, this information was used to construct a linear regression model as a foundational benchmark. Subsequently, the study focused on evaluating the performance of RFR, GPR, and GBM methodologies. This study aimed to determine the effectiveness of ensemble and Bayesian techniques in accurately predicting dynamic, hole-by-hole OB and UB conditions in underground stoping environments, where OB and UB vary spatially and form part of a three-dimensional predictive framework rather than a single averaged outcome. Leveraging recent advancements in predictive modelling, this research explores specific variables critical for predicting OB and UB in underground mining environments. Table 2 lists a detailed set of factors employed in developing the models for this analysis. The Journal of the Southern African Institute of Mining and Metallurgy
Integrating Bayesian, ensemble, and hybrid machine learning models Table 1 Summary of recent studies on dilution and ore loss prediction methods applied in underground mining Ref.
Parameters
(Liu et al., 2023)
Focus on geological conditions.
(Zhang et al., 2024)
Ground deformations, model and parameter uncertainties.
Methods Histogram gradient boosting tree (HGBT).
Results
Critique
Addressed rock burst prediction, improved dataset handling.
Lack of comparison with other methods.
Bayesian model averaging.
Enhanced estimation of tunnelling-induced deformations.
Focus on tunnelling, not specific to stope mining or UB/OB.
(Jang et al., 2015)
Geological variability, OB/ UB
Linear/nonlinear regressions, ANN.
Introduced uneven break and predicted unplanned dilution and ore loss.
Limited to simpler methods, lacking advanced ML applications or 3D models.
(Hong et al., 2023)
Point cloud data, 3D model for blasting.
3D Modelling and simulation using point cloud.
Simulated distribution of OB/UB.
The simplified model may not fully capture complex blasting dynamics.
(Adoko et al., 2022)
Stope design, stability graph method.
ANN-based classifiers.
Critiqued traditional methods, suggested ANN for stability design.
Limited testing of ANN models, lacks validation on large datasets.
(Cordova et al., 2022)
Stope/drift design, dilution assessment.
ELOS method, stope design assessment.
Assessed actual dilution, the impact of stope design on stability.
ELOS method limited by its reliance on traditional design metrics.
(Han et al., 2020)
Drillhole diameter and depth, powder factor, burden/spacing.
Random forest and Bayesian network.
Eliminated less impactful parameters for predicting fly rock.
Limited to surface mining and lack of geotechnical parameters.
(Chimunhu et al., 2024)
Stope geometry, geotechnical factors, geological factors.
Classification and regression tree (CART), hybrid model.
Achieved F1 score of 70% and 72% for the SSE-CART and PCA-CART with R2 of 72% and 93%, respectively.
The models utilised basic geological, geotechnical, and stope design attributes available during the early stages of mine planning, making them practical only for preliminary assessment.
(Yu et al., 2021)
Blast design, boundary measurement.
Support vector regression and Gaussian process regression.
Improved boundary control, reduced ore loss and dilution.
Lacks detailed validation metrics, requires highquality boundary data.
Analysis of reconciliation practices.
Identified inconsistencies in accounting for blasted ore left unrecovered in stopes. Highlighted the impact on mineral reserve and stope performance assessment.
Lacks quantitative data or case studies, primarily a discussion on reporting practices.
Identified that multiple factors simultaneously influence stability and dilution in open stopes, making it challenging to assess the effect of individual changes.
The study provides valuable insights into the factors affecting hanging wall dilution, it does not offer specific quantitative results or predictive models. The findings are based on observations from a single mine site, which may limit the generalisability of the conclusions.
(Ibarra-Gutierrez, Laflamme, 2021)
(Forster et al., 2007)
Stope limits, geotechnical and geological factors, unrecovered blasted ore.
Stope geometry, rock mass quality, stress conditions, cable support, blasting method, sequencing.
Detailed analysis of 3D stope survey data combined with geological assessment, focused on hanging wall stability.
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(Mottahedi et al., 2018)
(Himanshu et al., 2023)
(Papaioanou, Suorineni, 2016)
(Zhao, Jia'an, 2020)
(Hefni, Abdellah Wael, 2020)
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The ANFIS-PSO model outperformed the standard ANFIS model, achieving a higher coefficient of determination (R² = 0.961) and lower root mean square error (RMSE = 0.064), indicating improved predictive accuracy.
Although the models demonstrated high accuracy, they were developed based on specific case studies, which may limit their generalisability and only applicable to development areas.
Blasting design parameters, rock mass characteristics, stope geometry, and operational constraints.
Discusses innovative blasting techniques tailored for underground hard rock mining, focusing on optimising blast designs to enhance fragmentation, reduce overbreak, and improve overall operational efficiency.
Implementation of the discussed innovative blasting practices has led to improved fragmentation, reduced overbreak, enhanced safety, and increased productivity in underground hard rock mining operations.
The study provides insights into advanced blasting techniques, but it primarily offers qualitative assessments without extensive quantitative data or case studies to substantiate the claimed improvements. Further empirical research and detailed case analyses would strengthen the applicability of these practices across diverse mining scenarios.
Hydraulic radius (HR), modified stability number (N'), orebody width.
Statistical analysis of 226 open stope case studies from six Australian underground metalliferous mines using logistic regression and Bayesian likelihood discrimination to develop a quantitative dilution-based stability graph independent of orebody width.
The developed graph provides quantitative predictions of dilution levels, enabling mining engineers to design open stope sizes based on acceptable dilution thresholds, applicable to both narrow and wide orebodies.
While the model offers a more generalised approach than previous qualitative graphs, its reliance on data from Australian mines may limit its applicability in different geological settings without further validation. In addition, it only classifies stopes based on stability criteria (i.e., not predicting overbreak or underbreak).
Modified stability number (N'), hydraulic radius (HR), average borehole deviation, and powder factor.
Developed a three-layer back-propagation (BP) neural network with six neurons in the hidden layer to predict unplanned ore dilution, using equivalent linear overbreak slough (ELOS) as the output variable. The model was trained and tested on 120 datasets.
Achieved a high prediction accuracy with an R² of 0.9761 and a mean square error of 0.0001. When applied to a test stope in the Sandaoqiao lead–zinc mine, the model predicted an unplanned dilution of 0.717 m, closely matching the measured value of 0.70 m, resulting in a relative error of 2.4%.
While the model demonstrates high accuracy, its validation was limited to a single case study with only 120 data points. In addition, this study only focuses on dilution and did not consider ore loss.
Ore dip angle, stope width, stope height, In situ stress ratio
2D elasto-plastic numerical modelling to assess the impact of ore inclination and stope geometry on hanging wall (HW) stability and ore dilution.
Steeper ore dips (85°) improve hanging wall stability and reduce dilution, while wider and taller stopes increase instability and dilution. Ore dip angle has little effect on wall deformation.
The study provides valuable insights into the interplay between stope geometry and HW stability. However, it is based on 2D models, which may not capture all 3D complexities of actual mining scenarios.
Tunnel section area, perimeter holes powder factor (PPF), specific drilling (SD), spacing-toburden ratio (S/B), and rock mass rating (RMR).
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Developed predictive models using soft computing techniques, specifically adaptive neuro-fuzzy inference system (ANFIS) and a hybrid ANFIS optimised with particle swarm optimisation (ANFIS-PSO).
The Journal of the Southern African Institute of Mining and Metallurgy
Integrating Bayesian, ensemble, and hybrid machine learning models These predictors, instrumental in understanding the dynamics of OB and UB within the context of a specific mine site, include a range of variables from basic drill characteristics such as positioning and drillhole length to more complex drill and blast design and planning parameters like proximity from drillhole end to the planned stope shape, and burden and spacing. Additionally, factors like the location of primers in the blast plan, the void ratio relevant to drill and blast design stages, and explosive density are considered alongside parameters influencing stope stability including the hydraulic radius, Mathews stability number (Mathews et al., 1981), and the Q-value (Barton et al., 1974). RQD is rock quality designation, Jn is joint set number, Jr is the joint roughness number, Ja is the joint alteration number Figure 1 shows some key predictors impacting OB and UB, discussed in detail in Table 1, within an example stope from this study. This figure shows the OB and UB areas, the spatial arrangement of drillholes, the distance between adjacent drillholes, the distance between each drillhole end, and the planned stope shape boundary, the position of the final primer within the drillholes, and drillholes that do not break through to other underground areas or levels. OB and UB were quantified as the linear deviation between the designed stope boundary and the actual excavated surface obtained from CMS data. Positive values indicate material removed beyond
the design limit (OB), while negative values indicate insufficient breakage within the design (UB). These deviations were measured
Figure 1—Some of the key predictors affecting OB and UB in an example undeground mining stope
Table 2 Parameters used in the overbreak and underbreak prediction models Variable Number
Predictors
Symbol
Explanation
1
Drillhole sequence
HN
The drillhole number serves to establish the sequence in a ring blast, specifying the location of drillholes (numeric).
2
Explosive density
EXP
Density of explosive used in the specific drillhole (g/cm3).
3
Distance from end of drillhole to the stope shape
DIS
The distance from the end of the drillhole to the designed/planned stope shape (m).
4
Ratio of burden to spacing
BS
Burden: distance from point of blast to the free face (m). Spacing: distance between adjacent blastholes (m).
5
Primer location
PRIM
Distance from the last primer to the end of the drillhole (m).
6
Void ratio
VR
Ratio of void in relation to the blasted rock (%).
7
Powder factor
PF
Amount of explosive used per volume of rock (kg/tonne).
8
Paste-filled or not
PASTE
Whether the previous stope has been paste-filled or not (i.e., blasting against paste-fill or not).
9
Blasthole diameter
HD
Blasthole diameters ranging from 89 mm to 102 mm
10
Drillhole length
HL
Length of the designed drillhole in metres.
11
Breakthrough or nonbreakthrough drillhole
BT
Whether the drillhole breakthrough into the drift or level is above or not (value of 1 for breakthrough and 0 for non-breakthrough).
12
Ring dump
RD
Ring designed dump, where 0º is vertical, and 90º is horizontal.
13
Hydraulic radius
HR
Hanging wall hydraulic radius
14
Mathews stability number
N'
Mathews stability number, specifically assessing the stability of hanging wall in the dataset.
15
Q-system1
Q'
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Integrating Bayesian, ensemble, and hybrid machine learning models along each blasthole to enable hole-by-hole analysis and threedimensional prediction of boundary accuracy. Table 3 presents descriptive statistics for both predictor variables and the output parameter. It includes important measures such as the mean, median, standard deviation (StdDev), and the 25th and 75th percentiles, providing an overview of the data distribution. To illustrate the data composition and the structure of predictor-output relationships, Table 4 presents a small representative subset of the dataset used for model development, including sample drillholes with their corresponding predictor values and measured OB/UB outcomes.
Data filtering In the age of technology, where data is extensively utilised across various sectors, failing to detect anomalies can lead to unreliable results and misinterpretations (Falahi et al., 2023). In this study, outliers were detected using a distance-based method and the chi-squared distribution by measuring how far each point is from the data centre, based on the number of variables in the dataset (Filzmoser et al., 2008). Conventionally, the 95th or 99th percentiles serve as the cut-off benchmarks for identifying outliers. For this particular analysis and due to the size of the dataset, the 99th percentile was selected as the threshold criterion. The Mahalanobis distance test identified five data points with values exceeding the critical threshold of 30.577. After careful review, these points were confirmed as extreme but valid observations representative of genuine variability in OB and UB conditions rather than dataentry errors. Given the study’s focus on modelling the full range of operational variability, all observations were retained to preserve dataset integrity and ensure realistic predictive modelling. The correlation matrix in Figure 2 shows several key relationships among the variables and the output. The output variable shows the highest positive correlations with HR (0.4568) and PF (0.3924) and the highest negative correlations with VR (-0.3968) and PASTE (-0.3113), indicating these variables are potentially significant predictors for OB and UB. In addition, PF and VR, as well as PASTE and HL, show strong negative correlations of -0.75 and -0.71, respectively. In contrast, Q' and HR, as well as HR and PF, show positive correlations of 0.65 and 0.66, respectively. The highly correlated variables and multicollinearity will be investigated and discussed in the next section. Although multicollinearity was observed among some variables, principal component analysis (PCA) was not applied. PCA can effectively reduce dimensionality but transforms correlated variables into composite components, which could obscure the physical meaning
Figure 2—Correlation matrix representing also the relationships between variables 530
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of key geotechnical and blasting parameters (Jolliffe, Cadima, 2016). Because the purpose of this study is to interpret variable-specific effects on OB and UB rather than solely maximise variance, all variables were retained in their original form. Model interpretability was maintained using SHAP analysis, which quantifies each variable’s influence while preserving its engineering significance.
Methodology Multiple linear regression analysis This study used MLR as the foundational approach to define relationships between independent and target variables and evaluated the model’s performance using statistical metrics, such as RMSE and R2. Initially, the linear regression model produced an RMSE of 0.580, and R2 of 0.629. Collinearity analysis was conducted for linear regression as it can produce parameter estimates with incorrect signs or doubtful magnitudes, causing parameter estimates to vary widely with small data changes (Belsley et al., 1980). The VIF and tolerance are both widely used measures to assess the degree of multicollinearity among the independent variables in a regression model (O'Brien, 2007). The VIF for each predictor was calculated as:
where represents the coefficient of determination when predictor Xi is regressed on all other predictors (Montgomery et al., 2012). VIF was calculated for all predictor variables to detect the potential multicollinearity problem. The findings are presented in Table 5. VIF values exceeding 10 usually indicate that associated regression coefficients are poorly estimated because of multicollinearity, impacting the reliability of regression coefficients and model interpretability (Montgomery et al., 2012). This issue was noticed for the predictors VR, PF, PASTE, and HR, [A8.1] which showed VIF values of 16.2248, 12.5457, 16.5485, and 32.3352, respectively, indicating moderate to strong linear relationships with other predictors and potentially leading to high standard errors. Initially, to maintain key drill and blast predictors in the dataset, a decision was made to remove VR and PASTE from the four variables identified with VIF values higher than 10. This choice was based on the practical relationships between void ratio and hydraulic radius, as well as between paste and powder factor, ensuring that HR and PF remained in the analysis. The resulting linear regression model produced an R-squared of 0.439, and an RMSE of 0.742, indicating that while multicollinearity was partially mitigated, the model still explained only a moderate portion of the variance. Additionally, based on strong correlations observed in the correlation matrix, specifically between HR and PF, and HR and Q', a revised approach was tested by instead removing PASTE and HR while retaining VR and PF. The performance of this adjusted model declined slightly, with an R-squared of 0.399 and RMSE of 0.739. These results suggest that, although different variable exclusion strategies can address multicollinearity to varying degrees, the overall explanatory power of the linear model remains limited and additional factors or more advanced modelling techniques may be required to improve predictive accuracy. Furthermore, as explained by O'Brien, (2007), values of the VIF of 10, 20, 40, or even higher do not, by themselves, invalidate the results of regression analysis, The Journal of the Southern African Institute of Mining and Metallurgy
Integrating Bayesian, ensemble, and hybrid machine learning models Table 3 Basic statistical summary of predictors and output Predictors
Mean
Median
StdDev
25th percentile
75th percentile
HN
3.461
3
2.245
2
5
EXP
1.031
1
0.111
1
1
DIS
0.115
0
0.455
-0.24
0.455
BS
0.913
0.875
0.391
0.682
1.094
PRIM
1.259
1
1.041
0.425
2
VR
53.527
52
17.107
30
70
PF
1.758
1.96
0.447
1.35
2.24
PASTE
0.471
0
0.500
0
1
HD
90.818
89
4.521
89
89
HL
12.763
10.873
9.085
3.813
23.18
BT
0.186
0
0.390
0
0
RD
22.393
26.5
10.35
18.4
28.5
HR
4.574
4.88
0.873
4.6
5.15
N'
7.459
6.23
3.609
5.4
11.15
Q'
21.832
26.67
11.477
11.8
28.33
OB/UB
-0.241
0
0.956
-0.49
0.275
Table 4 Representative sample of drillhole-level data used in this study HN
EXP
DIS
BS
PRIM
VR
PF
PASTE
HD
HL
BT
1 8
RD
HR
1.2
0.5
0.86
0.00
30.00
1.96
1.00
89.00
1.40
1
0.13
2.38
0.50
48.80
2.24
0.00
89.00
1.80
0.00
0.00
0.00
28.50
1
1
0
0.80
2.00
52.00
2.26
0.00
89.00
1
1
0
0.81
1.00
74.00
1.35
1.00
89.00
7.14
0.00
29.30
7.85
0.00
35.00
3
1.2
0
0.30
7.00
64.00
1.39
0.00
89.00
20.67
0.00
1
1
0
0.92
1.00
76.00
1.12
1
1
-0.41
1.33
1.00
70.00
1.20
0.00
89.00
16.85
1.00
89.00
4.90
require the removal of independent variables from the analysis, suggest the need for ridge regression, or require the combination of independent variables into a single index. The results from the three linear regression equations highlight several important variables. In the first equation, significant predictors include PASTE, HR, and EXP, where higher negative and positive values are associated with increased UB and OB, respectively. In the second equation, after removing PASTE and VR due to multicollinearity, PF, EXP and HR remain crucial predictors, with PF showing a strong positive impact and EXP and HR showing strong negative impacts on OB and UB and in the third equation, EXP, BS and PRIM emerge as strong predictors. Table 6 displays the equation and results of the first and second linear regression models. OLS1: Ordinary least squares (OLS) is a standard method for estimating the coefficients in linear regression, aiming to minimise the sum of squared deviations (residuals) between data points and the regression line The Journal of the Southern African Institute of Mining and Metallurgy
N'
Q'
OB UB
4.88
6.23
28.33
0.18
5.15
11.15
11.8
-0.12
5.34
5.4
42.5
0.24
3.30
2.50
8.89
-2.10
29.50
4.60
5.40
26.67
0.92
0.00
28.30
5.00
14.10
28.33
-0.80
0.00
32.80
2.64
4.10
5.80
-0.60
Ensemble methods Tree-based methods such as RFR and GBM are generally less sensitive to multicollinearity compared to linear regressions. Each tree in these ensemble models splits the data based on a subset of features and individual trees are not affected by multicollinearity in the same way that linear models are (Cha et al., 2021). Similarly, Gaussian process regressions, being a non-parametric Bayesian approach, are primarily concerned with the covariance structure of the data rather than the individual impact of each predictor. Gaussian process uses kernel functions to define the similarity between data points, which helps in prediction (Rasmussen, Williams, 2006). In summary, while multicollinearity is a crucial consideration in linear models due to its impact on coefficient estimates and statistical tests, it is less of a concern in tree-based methods and GPR, which are the focus of this study. These models can handle multicollinearity without significant issues to their predictive capabilities. However, it may affect the interpretation of feature importance and, in the case of GPR, the model's VOLUME 126
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Predictor number
Predictors
VIF
1
HN
1.5641
2
EXP
1.7733
3
DIS
2.2493
configuration. In this study, RFR and GBM approaches were utilised due to their ability to handle complex, non-linear relationships and interactions between variables, leveraging ensemble learning techniques to improve the accuracy of OB and UB predictions. Both models combine outputs from multiple decision trees, effectively reducing variance and bias (Ghosal, Hooker, 2020). The model development process is discussed in more detail in the following sections.
4
BS
2.0038
Random forest regression
5
PRIM
2.535
6
VR
16.2248
7
PF
12.5457
8
PASTE
16.5485
9
HD
2.7901
10
HL
4.0637
11
BT
1.5483
12
RD
2.0897
13
HR
32.3352
14
N'
8.000
15
Q'
6.6617
Random forests are ensembles of decision trees built from bootstrap samples, where each tree is trained on a random subset of predictors (Cutler et al., 2011). The generalisation error converges as the number of trees increases and depends on both individual tree strength and inter-tree correlation. RFRs have become widely used in high-dimensional regression tasks because they require limited hyperparameter tuning and have a low tendency to overfit (Malhotra, Karanicolas, 2020). The model in this study was trained on 193 samples with 15 predictor variables. Hyperparameters were optimised using a grid search (number of trees = 50 – 200; minimum leaf size = 1 – 20; maximum splits = 10 – 100) and five-fold cross-validation. The optimal configuration,100 trees, 1 minimum leaf size, and 100 maximum splits, achieved the highest cross-validated R² = 0.682. The final model yielded R² = 0.812 (RMSE = 0.395) on the training set and R² = 0.749 (RMSE = 0.521) on the testing set (Table 7). Hyperparameter optimisation was
Table 5 Collinearity statistics results
Table 6 Linear regression models 1st Model
Method
Linear regression2
OLS1
2nd Model
Method
Linear regression2
OLS
3rd Model
Method
Linear regression3
OLS
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Equation
Equation
RMSE
R2
Linear regression1
OLS1
RMSE
R2
0.742
0.439
RMSE
0.739
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0.399
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Integrating Bayesian, ensemble, and hybrid machine learning models
Figure 3—3D plots of cross-validated R2 values for different combinations of hyperparameters in the random forest model
Table 7 Training and testing dataset performance metrics for the tuned RFR model Dataset
R2
RMSE
Training
0.812
0.395
Testing
0.749
0.521
conducted exclusively using the training data through a five-fold cross-validation grid search. The test set was withheld throughout this process and used only for final model evaluation to avoid information leakage. As illustrated in Figure 3, the cross-validated R² surfaces from the grid search confirm that increasing the number of trees and maximum splits improves model performance and stability. The figure also shows that very small or very large leaf sizes reduce accuracy. These patterns guided the selection of the optimal configuration (100 trees, minimum leaf size = 1, maximum splits = 100). Increasing the number of trees and maximum splits improved accuracy by reducing variance, whereas excessively large or small leaf sizes degraded performance (Hastie et al., 2009; Cutler et al., 2007). The selected configuration balanced stability and computational efficiency. Predictor importance analysis (Figure 4) identified N', BS, VR, and PF as the dominant predictors, linking geotechnical stability (e.g., Mathews stability number) with blast and design factors (burden-to-spacing ratio, powder factor, and void ratio). Various methods have recently been proposed to help users interpret the predictions of complex models such as SHAP analysis (Lundberg, Lee, 2017) that confirmed some variables as the most influential, with N' and HR increasing predicted OB and features such as PF and PRIM showing context-dependent effects (Figures The Journal of the Southern African Institute of Mining and Metallurgy
5a – 5b). In addition, negative SHAP values (in Figure 5b) mainly associated with lower VR, shorter HL, and lower PF, which drive UB. These effects are smaller in mean magnitude because UB cases are fewer and more localised. The SHAP baseline value represents the mean predicted OB/ UB across the training data, serving as the reference point from which each predictor’s contribution shifts the prediction toward OB (positive values) or UB (negative values).
Figure 4—Random forest variable importance plot
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Integrating Bayesian, ensemble, and hybrid machine learning models Gradient boosting machine The GBM model is an ensemble learning method that iteratively builds decision trees to correct the residuals of previous weak learners, thereby reducing bias and variance (Friedman, 2001). Similar to the random forest approach, the dataset was divided into 70% training and 30% testing to identify the best-performing model for predicting OB and UB. Hyperparameters were tuned using a grid search across number of trees = 50 – 200, learning rate = 0.01 –
Figure 5b—SHAP summary plot for the RFR model demonstrating variability
0.2, and maximum splits = 10 – 100, with five-fold cross-validation used to assess each configuration. The best model achieved a cross-validated R² = 0.731 with 200 trees, learning rate = 0.01, and 50 splits. Retraining produced R² = 0.897 (RMSE = 0.269) for the training set and R² = 0.686 (RMSE = 0.656) for the test set, while the full-dataset RMSE = 0.422. As illustrated in Figure 6, the 3D surface plots show how the GBM model’s performance (cross-validated R²) varies with tree number, learning rate, and split depth. Increasing the maximum number of splits generally improved accuracy, whereas lower learning rates produced more stable results. Optimal performance occurred with moderate-to-high tree counts, lower learning rates, and deeper splits. To address the small performance gap between training and test sets, a refined search (GBM2) was conducted using narrower hyperparameter ranges: up to 250 trees, learning rate = 0.015, and maximum splits = 60. The resulting model achieved cross-validated R² = 0.726, training R² = 0.946 (RMSE = 0.206), and test R² = 0.780 (RMSE = 0.503), with full-dataset RMSE = 0.323, indicating improved generalisation. The 3D surface plots for the refined model (Figure 7) confirm that increasing the number of trees and the learning rate enhances predictive strength, particularly when combined with higher split counts (40 – 60). The summary of both models is provided in Table 8, highlighting the improved robustness
Figure 6—3D surface plots visualising the performance of the GBM model based on different hyperparameter configurations
Figure 7—3D surface plots visualising the performance of the GBM2 model based on updated hyperparameter configurations 534
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Integrating Bayesian, ensemble, and hybrid machine learning models and lower error achieved by GBM2. Feature-importance analysis based on the SHAP framework is presented in Figures 8a and 8b. Figure 8a presents the mean absolute SHAP values for each predictor variable, quantifying their relative contribution to the GBM model’s predictions. The results show that N' is by far the dominant predictor (~ 2.28), applying a much stronger influence on model outputs than any other variable. BS follows as the second-most influential factor (~ 0.52), while predictors such as HD, PASTE, and Q' contribute only marginally. This hierarchy indicates that N' primarily governs the model’s predictive capability, with BS providing secondary influence related to burden-spacing effects. The SHAP summary plot (Figure 8b) further shows the direction and variability of these influences: higher N' values tend to increase predicted OB, while lower HL (shorter drillholes) and VR (smaller available void) are associated with UB. The spread of SHAP values reflects sample-specific variation, emphasising that the same predictor can have different effects under varying geological and blasting conditions.
Figure 8a—Mean absolute SHAP values for the optimised GBM model
Bayesian methods Gaussian process regression GPR is a non-parametric, probabilistic regression method that infers a distribution over possible functions rather than a single deterministic mapping (Rasmussen, Williams, 2006). This allows the model to provide both mean predictions and uncertainty bounds, making it valuable for applications requiring confidence estimation. By incorporating prior information through kernel functions, GPR captures complex relationships and quantifies prediction uncertainty (Snoek et al., 2012). The kernel function, defining the covariance between data points, is central to model performance. Four kernel types were evaluated in this study: squared exponential, Matérn 3/2, Matérn 5/2, and rational quadratic (Murphy, 2012). A grid search was applied to determine the optimal kernel based on cross-validated R² (Bergstra, Bengio, 2012). The initial model identified the Matérn 3/2 kernel as best, achieving cross-validated R² = 0.752. Using a 70/30 train-test split, the model produced R² = 0.933 (RMSE = 0.232) on training data and R² = 0.820 (RMSE = 0.452) on the test set, with an overall RMSE = 0.314. Figure 9 presents heatmaps of cross-validated R² values across kernel functions and hyperparameter combinations (length scale, sigma), where warmer colours denote higher performance. The best configurations included: squared exponential (length = 20, sigma = 1, R² = 0.752), Matérn 3/2 (1, 0.1, R² = 0.766), Matérn 5/2 (20, 0.1, R² = 0.761), and rational quadratic (20, 10, R² = 0.767). These results highlight the impact of kernel selection and parameter tuning on GPR performance. A refined model (GPR2) expanded the grid search to jointly optimise the kernel type, length scale, and sigma. The rational quadratic kernel achieved the best overall accuracy (cross-validated
Figure 8b—SHAP summary plot for the optimised GBM model demonstrating variability
Figure 9—Heatmap of cross-validated R2 values for different kernel functions
Table 8 Training and testing dataset performance metrics Models
Best R2 (5-fold CV)
Training set R2
Training RMSE
Initial GBM
0.731
0.897
GBM2
0.726
0.946
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set
Test set R2
Test set RMSE
Full RMSE
0.269
0.686
0.656
0.422
0.206
0.780
0.503
0.323
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Integrating Bayesian, ensemble, and hybrid machine learning models R² = 0.767). The final configuration (length = 20, sigma = 10) produced R² = 0.920 (RMSE = 0.237) for training and R² = 0.737 (RMSE = 0.601) for testing, with a full-dataset RMSE = 0.323. Comparatively, the initial Matérn 3/2 model generalised slightly better (test R² = 0.820, RMSE = 0.452), suggesting that while the refined model achieved higher training accuracy, it exhibited mild overfitting. As a result, the first model was selected as the preferred configuration for its balanced performance and stability. Figures 10a and 10b present the SHAP-based feature-importance results. The mean absolute SHAP values (Figure 10a) show VR, Q', and HL as the most influential predictors (~ 0.45-0.50), followed by RD, HN, and N' (~ 0.30). Less influential features such as EXP, DIS, BS, PRIM, PF, PASTE, HD, and BT exhibit moderate-to-low impact (~ 0.25). The SHAP summary plot (Figure 10b) further illustrates directionality: higher HL (longer holes) generally increases predicted OB, while smaller VR (limited available void) and lower HL correspond to UB conditions. The variability in SHAP distributions indicates that feature influence is context-dependent, reflecting local geological and blasting variability. The SHAP analysis not only quantifies the statistical influence of each predictor but also supports their physical relevance in underground stoping. For instance, High SHAP values for N' indicate that greater wall stability reduces the likelihood of excessive OB, consistent with established stability graph principles. Similarly, higher BS values correspond to more balanced explosive energy distribution, reducing both OB and UB. The VR parameter reflects the available void space for explosive expansion; insufficient void can lead to confined energy release and UB, while excessive void increases the risk of OB. These insights confirm that the model captures both the statistical and physical relationships driving stope performance, reinforcing its interpretability and field applicability.
Model evaluation and comparison – Bayesian and ensemble This study initially used multiple linear regression to define the relationships between independent variables and the target variable. The first linear regression model produced an RMSE of 0.580, an R² of 0.629, and an adjusted R² of 0.598. However, the second model's performance declined after addressing multicollinearity, resulting in an RMSE of 0.742 and R² of 0.439. The RFR model utilised 100 decision trees, with the best-performing model achieving a cross-validated R² of 0.682, a training set R² of 0.812, and an RMSE of 0.396. On the test set, it achieved an R² of 0.749 and an RMSE of 0.521, showing strong generalisability. Similarly, the first GBM model achieved a cross-validated R² of 0.731, with a training set R² of 0.898 and an RMSE of 0.270. However, its test set R² of 0.686 and RMSE of 0.656 suggested potential overfitting. To address this, a refined GBM model (GBM2) was developed, achieving a cross-validated R² of 0.726, a training set R² of 0.946 with an RMSE of 0.206, and a test set R² of 0.780 with an RMSE of 0.503, which indicated improvements in generalisability. The initial GPR model, utilising a basic grid search over kernel functions, identified the Matérn 3/2 kernel as the best-performing option, achieving an R² of 0.752, a training set R² of 0.933 with an RMSE of 0.233, and a test set R² of 0.820 with an RMSE of 0.452. A second GPR model with a refined hyperparameter tuning process using the rational quadratic kernel achieved a cross-validated R² of 0.767. Although it improved training set performance (R² = 0.921, RMSE = 0.237), its test set R² of 0.737 and RMSE of 0.600 indicated potential overfitting compared to the initial model. As shown in Figure 11, the initial 536
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Figure 10a—Mean absolute SHAP values for GPR model
Figure 10b—SHAP summary plot for GPR model demonstrating variability
GPR model became the preferred model due to its better test set performance and balanced fit. SHAP analysis was used to compare variable importance across all models. The bar chart in Figure 12 shows the mean absolute SHAP values for all variables across the three models (RFR, GBM, and GPR) and assesses their importance in predicting OB and UB. As highlighted in the chart, N', BS, and VR consistently appeared as the most important variables in all models. Table 9 further highlights the findings, showing that N' is the most important variable in both RFR and GBM, BS is the second most important in RFR and GBM, and VR is the top variable in GPR, where it is also the third most important in GBM. The prominence of N', BS, and VR across all three models is consistent with their physical significance and with field observations from the case study site. N' represents the modified Mathews stability number and is a composite indicator of rock mass quality, stress environment, and structural orientation. Higher N' values generally correspond to more favourable stability conditions, while lower values are commonly associated with increased perimeter damage and OB potential. The burden-to-spacing ratio directly influences energy distribution and confinement during blasting; deviations from the optimal BS range typically result in uneven breakout, either as excessive OB or UB. VR, defined as the void ratio available in front of the advancing blast, represents the amount of open space created by the previous extraction. It governs how effectively the next blast can expand into the available void. Insufficient void ratio restricts rock movement, increases confinement, and contributes to underbreak, while excessive void ratio reduces confinement and can contribute to uncontrolled OB (Lovitt, 2008). The alignment between these physical mechanisms, field observations, and the SHAP-derived importance rankings The Journal of the Southern African Institute of Mining and Metallurgy
Integrating Bayesian, ensemble, and hybrid machine learning models
Figure 11—Performance comparison of different Bayesian and ensemble models
Figure 12—SHAP values for all variables across models
Table 9 SHAP analysis results for all variables Variables
RFR
GBM
GPR
HN
0.26
0.28
0.31
EXP
0.21
0.22
0.24
DIS
0.23
0.27
0.25
BS
0.64
0.52
0.24
PRIM
0.31
0.24
0.24
VR
0.2
0.31
0.5
PF
0.29
0.22
0.24
PASTE
0.22
0.21
0.24
HD
0.22
0.2
0.23
HL
0.17
0.27
0.45
BT
0.23
0.26
0.25
RD
0.26
0.23
0.34
HR
0.57
0.23
0.24
N'
1
2.28
0.29
Q'
0.31
0.21
0.48
For instance, the refined GBM model achieved strong training accuracy (R² = 0.946) but showed signs of overfitting on the test set (R² = 0.780, RMSE = 0.503). Similarly, the refined GPR model, despite improved hyperparameter tuning, resulted in a test R² of 0.737, lower than its initial version. To address these limitations and leverage the complementary strengths of different algorithms, a hybrid modelling approach was employed. Table 10 summarises all seven hybrid models developed in this study. Each model is described in terms of its architecture, training and test performance (R² and RMSE), and a brief explanation of its structure and outcomes. The results show that while several hybrid configurations resulted in improved generalisation compared to basic models, the standalone GPR model remained the top performer. Nevertheless, the best hybrid model combining GPR, GBR, RFR, and ANN with a linear meta-learner achieved the highest test R² of 0.721 among all hybrid approaches. Figure 13 demonstrates the architecture of the best-performing hybrid model, which integrates four base learners of GPR, GBR, RFR, and ANN, each trained on the full input feature set. The individual predictions (ŷ₁ to ŷ₄) from these models are then passed to a linear regression meta-learner, which combines them to produce the final OB/UB prediction amount at each drillhole location (ŷ_final). Despite extensive experimentation with a range of hybrid models, including various combinations of GPR, GBR, RFR, and different meta-learners such as linear regression, gradient boosting, ANN, and Bayesian ridge, further improvements in test set R² plateaued. As shown in Table 10, the strongest generalisation performance (R² = 0.721, RMSE = 0.425) was achieved by Model 6, which incorporated all tuned base learners and used a linear regression meta-model. More complex configurations provided better training fits but failed to improve test R², indicating overfitting. Given this saturation point, additional attempts to improve R² were deemed unlikely to produce meaningful gains and were therefore not further pursued.
provides a consistent explanation for why N', BS, and VR emerged as the leading predictors of OB and UB.
Hybrid models While individual models such as RFR, GBM, and GPR demonstrated varying degrees of accuracy and generalisability, none offered consistently superior performance across all metrics. The Journal of the Southern African Institute of Mining and Metallurgy
Figure 13—Architecture of the best hybrid model (Model #6), using GPR, GBR, RFR, and ANN for OB/UB prediction via a linear regression metalearner VOLUME 126
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Integrating Bayesian, ensemble, and hybrid machine learning models Table 10 Performance summary of developed hybrid models Model #
Architecture
Train R²
Train RMSE
Test R²
Test RMSE
Description
1
GPR + GBR | LR meta
0.915
0.295
0.644
0.479
Initial hybrid model with GPR and GBR as base learners, LR as meta-learner. Reasonable generalisation, but did not surpass standalone GPR.
2
GPR + GBR | GBR meta
0.884
0.344
0.599
0.509
Same base learners, but nonlinear meta-learner (GBR). Performance dropped due to overfitting.
3
GPR + GBR + RFR | LR meta
0.903
0.315
0.560
0.533
Added RFR to increase diversity, but generalisation weakened, suggesting redundancy in base learners.
4
Tuned GPR + GBR + RFR | LR meta
0.900
0.319
0.679
0.455
All base learners tuned via GridSearchCV. Stronger generalisation, approaching standalone GPR.
5
Tuned GPR + GBR + RFR | ANN meta
0.877
0.354
0.702
0.438
ANN used as meta-learner. Reduced overfitting and improved test performance modestly.
6
Tuned GPR + GBR + RFR + ANN | LR meta
0.912
0.300
0.721
0.425
ANN added as a base learner. This was the strongest test performance among all hybrid models.
7
GPR + GBR | Bayesian
0.957
0.21
0.677
0.457
Hybrid model combining Bayesian (GPR) and ensemble (GBR) base learners with a Bayesian ridge meta-model. Strong training fit and moderate generalisation.
Across all configurations, the standalone GPR model remained the highest-performing test set R² (0.820), making it the most accurate and generalisable model in this study. While several hybrid models approached this level of performance, particularly the combination of GPR, GBR, RFR, and ANN with a linear metalearner (R² = 0.721), none fully surpassed GPR.
Conclusions This study focused on the application of Bayesian and ensemble methods to predict OB and UB in underground stoping operations. Initially, multiple linear regression was used to establish foundational relationships between variables, revealing significant predictors and multicollinearity issues. Addressing these issues resulted in a decline in the model’s performance, highlighting the limitations of linear methods to capture complex geotechnical and drill and blast relationships within the dataset. The RFR model significantly outperformed the MLR models, achieving a test R² of 0.749 and an RMSE of 0.521. This model's success can be 538
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related to its robustness against multicollinearity and its ability to capture complex interactions between variables. The RFR model was optimised using a grid search over ranges for the number of trees, minimum leaf size, and maximum number of splits. The best model was determined based on the highest cross-validated R² value, leading to the selection of 100 trees, a minimum leaf size of 1, and 100 maximum splits as the optimal hyperparameters. SHAP value analysis for the RFR model highlighted N', BS, and HR as the most impactful variables, indicating their important role in predicting OB and UB. The first GBM model achieved a cross-validated R² of 0.731. A refined GBM model (GBM2) was developed to address potential overfitting. The GBM2 model achieved a cross-validated R² of 0.726, a training set R² of 0.946 with an RMSE of 0.206, and a test set R² of 0.780 with an RMSE of 0.503. SHAP analysis for this model showed N', BS, and VR to be the most impactful variables. The GPR model, particularly with the Matérn 3/2 kernel, demonstrated strong predictive performance with a test R² of 0.820 and an RMSE of 0.452. As the refined GPR model The Journal of the Southern African Institute of Mining and Metallurgy
Integrating Bayesian, ensemble, and hybrid machine learning models showed signs of overfitting, the initial model's balanced fit and high predictive accuracy made it a preferred choice. SHAP analysis for the GPR model revealed that VR, Q', and HL were the most important variables to predict OB and UB. To further explore model performance, a series of hybrid stacking models were developed using various combinations of base learners, including GPR, GBR, RFR, and ANN, with meta-learners such as linear regression, ANN, and Bayesian ridge. Among the architectures trialled, the model that incorporated all four base learners with a linear regression meta-learner achieved the strongest generalisation, with a test R² of 0.721 and RMSE of 0.425. Deep stacking configurations and more complex meta-learners such as ANN and Bayesian ridge did not yield further improvements and showed signs of overfitting. Additional approaches, such as simple and performance-weighted averaging of tuned base model predictions, also failed to outperform the best hybrid configuration. These results demonstrate that while hybrid ensembles offer flexibility and robustness, there is a clear performance plateau when using the current feature set and data structure. In conclusion, ensemble methods, particularly RFR and GBM, demonstrated reasonable performance in predicting OB and UB in underground stoping. However, The Bayesian approach, represented by the GPR model, showed significant promise. SHAP value analysis provided valuable insights into feature importance, consistently identifying geotechnical and drill and blast design variables such as N', BS, VR, and HR as key predictors across all models. These findings reinforce the critical role of design parameters like burden and spacing, the Mathews stability number (focusing on hanging wall stability), as well as blasting parameters such as void ratio and hydraulic radius, in effectively controlling OB and UB in open stoping operations. While the models developed in this study provided strong predictive capabilities, several limitations remain. All models were trained and tested on historical data from different zones in a single mining operation, limiting the ability to generalise findings across different geological settings or operational conditions. Despite the extensive exploration of hybrid configurations, performance gains plateaued, suggesting that the current feature set may not fully capture all drivers of OB and UB. Future work should focus on expanding the dataset to include multiple operations with varying geological conditions, incorporating additional process and operational variables, and exploring real-time data integration to improve model adaptability.
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The Journal of the Southern African Institute of Mining and Metallurgy
Assessment of coal spontaneous combustion potential using statistical and machine learning approaches by H. Ghasemzadeh1, H. Madani1, F. Sereshki², S. Afraei1
Affiliation: 1 Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran 2 Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran Correspondence to: H. Madani Email: madani.hasan.aut@gmail.com
Dates:
Received: 21 Apr. 2026 Revised: 26 Jun. 2025 Accepted: 14 Aug. 2026 Published: Sep. 2026 How to cite: Ghasemzadeh, H., Madani, H., Sereshki, F. Afraei, S. 2026. Assessment of Coal Spontaneous Combustion Potential Using Statistical and Machine Learning Approaches. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 9, pp. 541 – 558 DOI ID: https://doi.org/10.17159/2411-9717/3974/2026 ORCiD: H. Ghasemzadeh https://orcid.org/0009-0009-6308-7872 H. Madani https://orcid.org/0009-0002-6496-1807 F. Sereshki https://orcid.org/0000-0002-2626-6865 S. Afraei https://orcid.org/0000-0002-8476-6358
Abstract
Spontaneous combustion of coal remains one of the most critical safety challenges in underground coal mining, posing significant risks to personnel, operational continuity, and the environment. Reliable prediction of coal self-heating potential is therefore essential for effective risk management throughout the mine life cycle. This study presents a data-driven framework for evaluating coal spontaneous combustion potential based on intrinsic coal properties derived from proximate and maceral analyses. A comprehensive dataset comprising 165 coal samples was compiled from published experimental studies, representing a wide range of coal characteristics. The crossing point temperature was adopted as a quantitative indicator of spontaneous combustion tendency. To investigate the complex relationships between coal properties and crossing point temperature, a combination of statistical and intelligent modelling approaches was employed, including nonlinear regression, artificial neural networks (multilayer perceptron and radial basis function), and a decision tree classifier. The results demonstrate that the relationship between intrinsic coal parameters and spontaneous combustion potential is inherently nonlinear and multivariate. While nonlinear regression achieved acceptable predictive performance (R² = 0.82), its mathematical complexity limits practical applicability. Among the developed intelligent models, the radial basis function neural network achieved the lowest prediction error (approximately 5.7%). In parallel, the decision tree model achieved a classification error of about 8% while providing a transparent and interpretable structure for practical decision-making. A key contribution of this study is the development of an integrated and interpretable classification framework based on readily available proximate and maceral analysis parameters, addressing the practical limitations associated with the lack of detailed elemental data in many mining operations. The developed framework was further evaluated using independent field samples from the Parvadeh coal mines (Tabas coalfield, Iran), confirming its applicability under real mining conditions. Unlike previous studies that mainly focused on individual algorithms or laboratory-scale datasets, this study establishes a comparative modelling framework by integrating multiple statistical and intelligent approaches using a comprehensive database of 165 coal samples and independent field validation. The proposed approach provides a practical and interpretable tool for assessing coal spontaneous combustion potential, with potential applications in mine design, operational planning, and post-closure risk management.
Keywords
Coal spontaneous combustion potential, crossing point temperature, intrinsic coal properties, classical statistical models, intelligent modelling techniques
Introduction Coal remains one of the most widely utilised fossil fuels worldwide and is inherently prone to combustion. As a result, underground coal mining environments are characterised by complex operational conditions associated with a wide spectrum of safety hazards that must be systematically identified and managed from the early stages of mine design through extraction, operation, and continuous monitoring. Accordingly, the development of effective strategies for hazard prediction, prevention, and control is essential to ensure safe and sustainable mining practices. Among the major hazards encountered in underground coal mining—such as roadway collapse, gas explosions, surface subsidence, and rock bursts—coal spontaneous combustion is widely recognised as one of the most critical due to its direct role in initiating mine fires and causing severe operational disruptions (Akgün, Arisoy, 1994; Nugroho et al., 2000; Smith, Glasser, 2005; Beamish, Arisoy, 2008; Saffari et al., 2017). Coal spontaneous combustion is a complex physicochemical process primarily governed by lowtemperature oxidation reactions. This process may occur over a broad range of ambient conditions and
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Assessment of coal spontaneous combustion potential using statistical and machine can progressively lead to self-heating and eventual ignition in the absence of any external heat source (Demir et al., 2020). The global distribution of coal fire zones and regions affected by spontaneous combustion is illustrated in Figure 1. The oxidation of coal is an exothermic process that results in a gradual temperature rise due to changes in its intrinsic thermal and chemical properties, which may ultimately lead to combustion. The fundamental mechanisms underlying coal oxidation are schematically presented in Figure 2. Despite extensive research efforts, a comprehensive understanding of this phenomenon remains challenging due to the complex interactions among multiple influencing parameters (Wang et al., 2003). The impacts of coal spontaneous combustion are far-reaching and multifaceted, including loss of human life, environmental
Figure 1—Countries and fire zones that have experienced spontaneous combustion problems (Demir et al., 2020)
Figure 2—Fundamental phenomena occurring in coal oxidation process (Wang et al., 2003)
degradation, greenhouse gas emissions, land subsidence, mine closure, infrastructure damage, production interruptions, and increased operational and remediation costs. These consequences underscore the importance of adopting responsible coal mining practices that align environmental, economic, and social considerations within a sustainable development framework. A conceptual overview of responsible coal mining is presented in Figure 3 (Michaylov, 2002). Coal spontaneous combustion can occur in a variety of settings, including underground and surface mines, abandoned workings, coal stockpiles, waste dumps, and transportation systems such as maritime shipping (Michaylov, 2002). Consequently, continuous risk assessment is required throughout the entire mining life cycle, encompassing mine design, operation, closure, and post-mining reclamation. Such assessments typically begin with evaluating the spontaneous combustion potential of coal and identifying the key parameters influencing this behaviour (Lang, Fu-Bao, 2010; Wang, Tong, 2020; Ahamed et al., 2016). Accurate evaluation of coal spontaneous combustion potential requires reliable and robust methods capable of handling large datasets, minimising prediction uncertainty, and maintaining applicability under real mining conditions. Although numerous studies have explored the influence of various parameters on spontaneous combustion, many existing models suffer from important limitations, including reliance on limited datasets, dependence on complex or less accessible input parameters, reduced interpretability, and limited applicability in practical mining environments. In real-world mining operations, access to detailed coal characterisation data is often constrained, particularly when rapid and cost-effective assessments are required. In this context, proximate analysis parameters are generally more accessible and routinely measured compared to detailed elemental data. Despite their practical advantages, the systematic use of these parameters for developing accurate, reliable, and interpretable predictive models has not been sufficiently addressed in the existing literature. To overcome these limitations, this study proposes a datadriven and practically applicable framework for evaluating coal spontaneous combustion potential based on intrinsic coal properties derived primarily from proximate and maceral analyses. A comprehensive dataset comprising 165 coal samples was compiled from published experimental studies to enhance model robustness and generalisability. The crossing point temperature (CPT), a widely accepted laboratory-based index for assessing spontaneous combustion tendency, was adopted as the primary evaluation criterion in this study (Misra, Singh, 1994). The classification of coal based on CPT values is presented in Table 1. Table 1 Classification of CPT values and the status of spontaneous combustion of coal potential (Misra, Singh, 1994)
Figure 3—Responsible mining of coal 542
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The spontaneous combustion of coal potential
CPT value (Celsius)
Class
low
>160
1
medium
140-160
2
high
120-140
3
The Journal of the Southern African Institute of Mining and Metallurgy
Assessment of coal spontaneous combustion potential using statistical and machine A combination of classical statistical techniques and advanced intelligent modelling approaches was employed, including nonlinear regression, artificial neural networks (multilayer perceptron and radial basis function), and a decision tree classifier. This integrated methodology enables both high predictive accuracy and improved model interpretability. The main contributions of this study can be summarised as follows: (1) development of a comprehensive database containing 165 coal samples by integrating published experimental CPT data and intrinsic coal characteristics; (2) systematic comparison of classical statistical models and intelligent approaches, including regression models, artificial neural network (ANN)-based methods, radial basis function (RBF) networks, and decision tree algorithms, within a unified modelling framework; (3) development of an interpretable spontaneous combustion classification decision tree (SCCDT) model that provides a balance between predictive accuracy, simplicity, and practical usability; and (4) validation of the proposed framework using independent coal samples from the Parvadeh Tabas coal mines to evaluate its applicability under real mining conditions. The proposed framework provides a reliable, accurate, and interpretable decision-support tool for assessing coal spontaneous combustion potential and supports improved safety management and risk mitigation strategies in underground coal mining operations.
Literature review Over the past century, extensive research has been conducted on the spontaneous combustion of coal due to its critical implications for mine safety, economic performance, and environmental sustainability. Early foundational studies, such as those by Parr and Hilgard (1925), identified pyrite oxidation as a key contributor to self-heating, thereby establishing the fundamental chemical mechanisms governing spontaneous combustion. Subsequent investigations demonstrated that coal composition plays a decisive role in determining susceptibility to self-heating, with different coal ranks exhibiting significantly different reactivity under similar environmental conditions (Guney, 1968; Feng et al., 1973). These findings positioned intrinsic coal properties as the central focus of spontaneous combustion research across the entire mining lifecycle, including post-mining conditions (Şensöğüt, 2011). Between the 1970s and early 2000s, research expanded to incorporate both intrinsic and operational factors influencing spontaneous combustion. Early studies examined the effects of moisture content (Hodges, 1963), coalification degree (Chandra, Prasad, 1990), maceral composition (Cotterell, 1997), and ventilation-induced heat accumulation (Mitchell, 2003; Yuan, Smith, 2012), reinforcing the dominant role of intrinsic coal properties in governing oxidation behaviour. Concurrently, several investigations addressed mining-related and geological parameters—including gob characteristics, air leakage pathways, and longwall face advance rate (Zhang, Qin, 2009; Taraba, Michalec, 2011; Xia et al., 2015)—demonstrating that operational conditions can significantly amplify or mitigate spontaneous combustion risk. A summary of these influencing factors is presented in Table 1. Building on these foundations, more systematic investigations over the past two decades evaluated spontaneous combustion potential across various mining environments, including underground mines, open-pit operations, and coal stockpiles. Empirical and semi-analytical relationships between coal properties The Journal of the Southern African Institute of Mining and Metallurgy
and self-heating indices were developed, and structured risk assessment frameworks—often based on historical fire data and hierarchical decision models—were introduced, particularly in Chinese coal mining practices (Lang, Fu-Bao, 2010; Banerjee, 1985; Smith, Lazzara, 1987; Kaymakci, Didari, 2002). From approximately 2010 onward, the literature reflects a transition toward quantitative and mechanistic modelling approaches. Beamish and Beamish (2008) and Beamish and Sainsbury (2010) proposed empirical self-heating rate equations to quantify oxidation kinetics, Liu and Qin (2017) developed multiphysics coupling models integrating oxidation reactions, heat transfer, and gas migration processes in longwall gob environments. Thermodynamic analyses further highlighted the catalytic role of pyrite in accelerating oxidation reactions and heat generation (Yang et al., 2019). A conceptual representation of these coupled processes is illustrated in Figure 2. Between 2018 and 2023, rapid methodological advancements emerged with the adoption of data-driven and intelligent modelling techniques. Studies by Onifade and Genc (2018a; 2020), Saffari et al. (2019), and Gbadamosi et al. (2021) demonstrated strong correlations between proximate analysis parameters—including fixed carbon (FC), ash content (AC), moisture content (MoC), volatile matter (VM), and pyritic content (PC)—and spontaneous combustion indices. Additional studies investigated the influence of external factors such as drying methods (Zhang et al., 2018; Zhao et al., 2016), particle size distribution (Więckowski et al., 2020), and moisture migration mechanisms (Wu et al., 2020), further refining the understanding of coal reactivity. Recent research has increasingly incorporated advanced computational tools and digital technologies. These include computational fluid dynamics (CFD) modelling for simulating airflow and heat transfer (Taraba, Michalec, 2011), computer-based mining system simulations (Meshkov, 2019), and internet of things (IoT)-based classification systems for real-time monitoring (Mishra, Gupta, 2024). Furthermore, machine learning approaches— including neuro-fuzzy systems (Sahu, Mahapatra, 2013), logistic regression models (Kursunoglu, Gogebakan, 2022), support vector machine-based algorithms such as SASVM (Deng et al., 2021), and multi-parameter optimisation frameworks (Xu, Zhang, 2023) —have significantly enhanced prediction accuracy and enabled automated risk classification. A comparative overview of selected predictive models is presented in Table 2. More recent studies have expanded the research scope to include integrated analyses of coal mine fires, their environmental impacts, and preventive strategies. Accurate prediction of spontaneous combustion potential has been identified as a critical component of fire prevention systems. Consequently, advanced data-driven techniques have gained increasing attention. For instance, random forest algorithms have been applied to predict spontaneous combustion potential with high accuracy in large datasets. Additionally, several studies have emphasised the critical role of intrinsic coal properties—particularly moisture content—in controlling oxidation behaviour. Comprehensive analyses have shown that increases in fixed carbon, moisture, hydrogen, volatile matter, nitrogen, and pyrite content, combined with lower ash content, tend to enhance spontaneous combustion susceptibility (Saffari et al., 2017, 2020; Song, Kuenzer, 2014; Tosun, 2017; Lei et al., 2018; Onifade, Genc, 2018b). In parallel, methodological developments have led to a wide range of approaches for assessing spontaneous combustion VOLUME 126
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Assessment of coal spontaneous combustion potential using statistical and machine potential, including laboratory-based experimental techniques, mathematical modelling, probabilistic risk assessment methods, and artificial intelligence-based frameworks. Comparative studies indicate that machine learning techniques often outperform traditional empirical models; for example, random forest algorithms have demonstrated superior predictive performance compared to multilayer perceptron (MLP) networks when applied to large coal datasets (Onifade, Genc, 2020; Said et al., 2021; Deng et al., 2021; Zhang et al., 2022). More recently, emerging technologies have been integrated into comprehensive risk assessment frameworks. IoT-based systems have been proposed for classifying coal seam self-heating behaviour using intrinsic coal properties as input parameters. In addition, risk-based analytical techniques such as fault tree analysis (FTA) have been applied to evaluate spontaneous combustion risk under complex mining conditions (Mishra, Gupta, 2024; Yetkin et al., 2024; Wang et al., 2023). Based on the reviewed studies, it is evident that coal spontaneous combustion is a complex, multi-factor phenomenon governed by a wide range of interacting variables. These influencing factors can be broadly categorised into three main groups: (i) intrinsic coal properties, (ii) geological characteristics of coal seams, and (iii) mining and operational parameters. Among these, intrinsic properties—including moisture content, ash yield, volatile matter, fixed carbon, pyrite content, and petrographic composition—have consistently been identified as the most influential determinants of spontaneous combustion potential across different spatial and temporal scales. As summarised in Table 2, the parameters influencing coal spontaneous combustion potential can be systematically categorised into three major groups based on the direction and nature of their effects: intrinsic coal properties, geological characteristics, and mining-related factors. This classification provides a structured framework for understanding whether each parameter contributes positively or negatively to spontaneous combustion tendency. From the perspective of intrinsic coal properties, coal rank plays a pivotal role in determining reactivity. Lower-rank coals typically exhibit higher porosity and greater oxygen accessibility, thereby increasing their susceptibility to spontaneous combustion. Pyrite content has been widely recognised as a catalytic factor that accelerates oxidation reactions and enhances heat generation. Moisture content presents a dual and competing influence: while it promotes oxidation and heat accumulation under certain conditions, moisture evaporation can simultaneously absorb heat and locally suppress combustion processes. Oxygen availability,
whether through natural permeability or ventilation leakage, directly intensifies oxidation reactions and increases combustion risk. Petrographic composition further governs coal reactivity, with vitrinite- and liptinite-rich coals exhibiting higher susceptibility compared to inertinite-dominated coals. In contrast, ash content generally reduces spontaneous combustion potential by acting as a thermal sink that absorbs oxidation heat. Methane presence tends to inhibit oxygen diffusion within the coal matrix, thereby reducing oxidation intensity. Coal brittleness increases the exposed reactive surface area, which enhances oxidation kinetics. Additionally, inorganic constituents exhibit contrasting roles, where certain salts suppress oxidation, while alkali elements such as sodium and potassium promote it. Volatile matter content is consistently associated with increased spontaneous combustion potential. From a geological standpoint, increasing seam depth results in higher in situ stresses, leading to coal fracturing and enhanced permeability, both of which facilitate oxygen ingress and oxidation. Greater seam thickness increases the exposed coal surface area, thereby intensifying oxidation reactions. Steeper seam dip angles often reduce recovery efficiency and increase residual coal, indirectly elevating combustion risk. Geological discontinuities such as faults, clastic dikes, clay veins, and intrusive bodies create pathways for air leakage and further increase exposure surfaces. Multi-seam mining operations can induce stress redistribution and fracturing in adjacent seams, thereby amplifying spontaneous combustion susceptibility. Additionally, the presence of adjacent oil shales or geothermal heat sources can elevate seam temperature and accelerate self-heating processes. Mining-related parameters also play a critical role in controlling spontaneous combustion risk. Mining methods that achieve higher recovery rates and minimise residual coal generally reduce combustion potential. However, mining-induced pressure differentials may promote air leakage into goaf areas, thereby increasing oxidation rates. Lower face advance rates prolong coal exposure to oxygen, which significantly increases combustion risk. Ventilation systems exhibit a dual influence: insufficient airflow limits heat dissipation, while excessive airflow enhances oxygen supply and accelerates oxidation. Air leakage into caved zones is one of the most critical contributors to spontaneous combustion in underground mines. Furthermore, higher extraction ratios reduce the amount of residual coal and consequently decrease oxidation potential. It is important to note that inconsistencies in previous studies
Table 2 The parameters affecting coal spontaneous combustion Parameter type
Parameters with a direct relationship
Parameters with inverse relationship
1
Intrinsic factors
Pyrite, moisture, oxygen, vitrinite, liptinite, coal brittleness, potassium and sodium, and volatiles.
Degree of charring, inertinite, ash, gassiness, and salt family.
2
Geological factors
Depth, thickness, layer slope, fault presence, and multi-layer extraction.
3
Exploitation factors
Direction of advance, intensity of ventilation air flow, and air leakage.
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Complete extraction, advance speed, type of side ventilation system, and coal extraction coefficient.
The Journal of the Southern African Institute of Mining and Metallurgy
Assessment of coal spontaneous combustion potential using statistical and machine often arise from the isolated evaluation of individual parameters without considering their interactions. Such approaches fail to capture the complex, nonlinear, and overlapping relationships among influencing factors. This limitation highlights the necessity for integrated modelling frameworks capable of simultaneously incorporating multiple parameters and their interdependencies. Given the wide range of influencing factors and their complex interactions, evaluating spontaneous combustion potential using single-parameter or traditional empirical approaches is insufficient. Many existing studies are limited by small datasets, site-specific conditions, or simplified modelling assumptions, which restrict their general applicability. Therefore, there is a clear need for robust, data-driven, and integrated modelling approaches that can handle multiple variables, capture nonlinear relationships, and provide reliable predictions across different mining conditions. Accordingly, this study aims to develop a comprehensive predictive framework for assessing coal spontaneous combustion potential based on intrinsic coal properties. The approach integrates classical statistical methods, including linear and nonlinear regression models, with advanced intelligent techniques such as artificial neural networks (ANN), radial basis function (RBF) models, and decision tree algorithms. The crossing point temperature (CPT) is used as a quantitative indicator for classification and validation. By systematically comparing model performance in terms of accuracy, robustness, and practical applicability, this study seeks to provide a reliable decision-support tool for improving safety management in underground coal mining operations. The remainder of this paper is organised as follows. A presentation of the data analysis and evaluation procedures is followed by a description of the development of classical statistical and intelligent models. The section that follows provides model validation results and finally, the findings are discussed the main conclusions presented.
Analysis and evaluation of data affecting the spontaneous combustion of coal potential The analysis and evaluation of parameters influencing coal spontaneous combustion potential were carried out through a series of systematic and sequential steps. Based on the literature review, intrinsic coal properties have received the greatest research attention, with 32 studies focusing on intrinsic characteristics, compared to only 2 studies on geological characteristics and 12 studies addressing mining-related factors. This distribution highlights not only the dominant role of intrinsic coal properties but also the unequal consideration of different influencing parameters. Table 3 presents the distribution of studies on coal spontaneous combustion potential from 1980 to the present. As shown in Table 3, and as discussed in the literature review section, intrinsic coal characteristics have been investigated far
more extensively than geological and mining-related factors in the assessment of spontaneous combustion potential. Therefore, this study primarily focuses on intrinsic coal properties as the main input variables for model development. The selection of input variables was performed based on a combination of previous research findings, physicochemical understanding of coal spontaneous combustion mechanisms, and practical data availability from operating mines. In this study, intrinsic coal parameters obtained from proximate analysis and petrographic characterisation were selected because these parameters directly influence oxygen accessibility, oxidation reactivity, and heat generation during the self-heating process. Although elemental analysis and other advanced characterisation techniques can provide additional information, such data are not consistently available for practical mine applications. Therefore, the selected variables represent a balance between scientific relevance and operational applicability. Feature reduction techniques such as recursive feature elimination or principal component analysis were not applied because the selected parameters were already established as influential factors in previous studies and were required to maintain physical interpretability of the developed models.
Statistical analysis of intrinsic characteristics of coal Previous studies have applied a wide range of approaches to evaluate and predict coal spontaneous combustion potential, ranging from conventional statistical techniques—such as linear and nonlinear regression models—to advanced intelligent methods, including artificial neural networks (ANN), radial basis function (RBF) networks, decision trees, random forests, and hybrid machine learning models. While classical statistical models offer simplicity and interpretability in analysing parameter relationships, they are often insufficient for capturing complex nonlinear interactions. To evaluate coal spontaneous combustion potential, a comprehensive statistical analysis of intrinsic coal properties was conducted to establish relationships between these characteristics and crossing point temperature (CPT) values. For this purpose, a total of 165 datasets were collected from previously published studies covering various underground coal mines. In all referenced studies, moisture, ash, volatile matter, and fixed carbon contents were determined using proximate analysis in accordance with ASTM standards. Table 4 summarises the results of proximate analysis, petrographic maceral composition, and CPT values for the collected coal samples. The frequency distribution of CPT values is illustrated in Figure 4, with CPT values presented in the final column of Table 4. As shown, the dataset consists of 165 samples, with CPT values ranging from approximately 70 °C to 200 °C. The mean CPT value is 153 °C, with the first and third quartiles equal to 140 °C and 174 °C, respectively.
Table 3 Distribution of studies on the spontaneous combustion of coal potential from 1980 to the present Characteristics affecting the spontaneous combustion of coal potential
Intrinsic characteristics
Geological characteristics
Mining characteristics of coal
Number of studies
32
2
12
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Figure 4—Frequency distribution chart of CPT values
Based on the CPT frequency distribution, approximately 31 samples are classified as having high spontaneous combustion potential, 86 samples fall within the medium category, and 48 samples are categorised as having low spontaneous combustion potential. Table 4 presents descriptive statistical measures, including mean, standard deviation, first quartile, and third quartile, for the eight primary intrinsic parameters considered in this study. Furthermore, Figure 5 illustrates the frequency distribution of intrinsic coal characteristics across the dataset, confirming the total number of 165 samples. Before model development, 25 samples were randomly excluded from the original dataset and completely reserved as an independent internal testing dataset. These samples were not involved in model training, parameter adjustment, or model selection. The remaining samples were used for developing the predictive models. Furthermore, independent field data collected from the Parvadeh Tabas coal mines were used exclusively for external validation and were not included in any stage of model development or evaluation.
Cross-correlation of intrinsic coal characteristics and experimental CPT values
In this section, the relationship between intrinsic coal characteristics and experimentally measured CPT values was investigated using univariate regression analysis. Both linear and nonlinear regression models were examined for each intrinsic parameter, and the model with the highest coefficient of determination (R²) was selected as the representative relationship. The results indicate that none of the intrinsic coal parameters exhibit a purely linear relationship with CPT, which is widely used as an indicator of coal spontaneous combustion potential. This nonlinearity can be attributed to the simultaneous and interacting effects of multiple intrinsic parameters governing coal oxidation behaviour. As illustrated in Figure 6, moisture content exhibits a nonmonotonic relationship with CPT. Specifically, spontaneous combustion potential decreases with increasing moisture content up to approximately 10%, after which further increases in moisture lead to higher combustion potential. A similar trend is observed for ash content, where CPT values increase when ash exceeds approximately 21%. Volatile matter demonstrates a threshold behaviour around 18%, while fixed carbon shows a turning point near 40%. Pyrite content initially reduces CPT values up to approximately 5%, followed by an increase in spontaneous combustion potential at higher concentrations. Petrographic components also display nonlinear relationships with CPT. Vitrinite content increases combustion potential up to approximately 30%, beyond which higher proportions reduce susceptibility. Liptinite follows a similar pattern with a turning point around 4%, while inertinite content increases combustion potential up to approximately 70%, after which the tendency declines. The univariate regression results further indicate that, when considered independently, pyrite content exhibits the strongest correlation with CPT (R² ≈ 0.59), whereas ash content and fixed carbon show the weakest relationships (R² ≈ 0.04). As demonstrated in Figure 6, none of the intrinsic parameters alone can adequately explain the variability in CPT values, and the predictive capability of single-variable models remains limited. Therefore, to achieve a more accurate and realistic
Table 4 Results of proximate analysis, petrographic maceral analysis, and CPT values of coal data Intrinsic characteristics- %
Statistical Variable Mean
CPT 153
Inertinite
Liptinite
Vitrinite
Pyrite
Fixed carbon
Volatile matter
Ash
Moisture
36.5
7.6
55.9
2
44
23.3
25.6
5.1
Median
149
35.3
7.9
57.4
1.8
44.3
23.2
23
3.2
Mode
180
0
11.5
72.1
0
38.1
18.5
41.2
2.2
Standard deviation
26.6
21.1
4.0
18.2
1.9
11.1
6.5
13.9
5.3
Sample variance
714
4.5
0.2
3.3
0.03
1.3
0.4
2
0.3
Range of variation
131
90
17
84
12
60
42
77
31
The minimum value
68.9
0
0.6
2.4
0
7.5
2
2.2
0.5
The maximum value
200
90.4
17.9
86.6
12.1
67.4
44
79.3
31.9
the first quartile
140
24.5
4.2
47.1
0.5
38
18.5
15.3
2
the third quartile
174
48.8
9.6
65.2
2.7
51.6
28
32.5
6.3
Number of values
165
165
165
165
165
165
165
165
165
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Figure 5—Frequency distribution chart of intrinsic characteristics values of coal The Journal of the Southern African Institute of Mining and Metallurgy
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Figure 6—Relationship between intrinsic coal characteristics and CPT values
understanding of coal spontaneous combustion behaviour, and considering the complex and multifactorial nature of coal selfheating, multivariate regression analysis was adopted in the next stage of this study. This approach allows for capturing the combined effects and interactions among multiple parameters, thereby providing a more robust foundation for subsequent modelling using advanced intelligent techniques.
Implementation and development of models In this section, a comprehensive set of statistical and intelligent modelling approaches was developed and systematically evaluated to identify a robust and reliable framework for predicting coal spontaneous combustion potential. The applied methods were categorised into two main groups: classical statistical approaches and artificial intelligence-based techniques. The selection of modelling approaches was based on the specific characteristics of coal spontaneous combustion prediction, 548
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including nonlinear relationships among variables, limited availability of experimental data, and the need for both predictive accuracy and practical interpretability. Classical regression models were initially considered because they provide a quantitative understanding of relationships between intrinsic coal properties and CPT values and serve as baseline models for evaluating predictive improvements. Artificial neural network (ANN)-based models were selected due to their strong capability in approximating nonlinear and complex interactions among multiple input variables without requiring predefined mathematical relationships. In particular, multilayer perceptron (MLP) networks were considered as a general nonlinear approximation approach, whereas radial basis function (RBF) networks were selected because of their efficient learning capability and suitability for problems involving nonlinear mapping with limited datasets. In addition, decision tree modelling was employed because The Journal of the Southern African Institute of Mining and Metallurgy
Assessment of coal spontaneous combustion potential using statistical and machine Table 5 5 Results of implementing multiple linear regression Unstandardised
coefficients
Beta
Std. Error
Constant
192.834
10.233
Moisture
-31.247
25.669
Volatile matter
-4.052
Fix carbon
Parameter
Standardised
coefficients t
Sig
Tolerance
VIF
18.844
0.000
-0.063
-1.217
0.225
0.804
1.244
20.332
-0.010
-0.199
0.842
0.878
1.139
-22.016
12.072
-0.092
-1.824
0.070
0.836
1.197
Pyrite
-520.916
73.943
-0.362
-7.045
0.000
0.807
1.239
Liptinite
-324.848
52.006
-0.491
-6.246
0.000
0.345
2.900
Inertinite
19.354
9.393
0.153
2.061
0.041
0.386
2.592
Table 6 Results of implementing nonlinear multiple regression
Beta
examined, and the final model was selected considering both classification accuracy and interpretability. Due to the large number of evaluated configurations, only the optimal model structures and their corresponding performance results are presented to maintain the clarity and conciseness of the manuscript.
Parameter
Model grade
Constant value
-
Moisture
3
Linear and nonlinear multiple regression
Ash
3
Volatile matter
3
Fix carbon
2
Pyrite
3
Initially, multiple linear regression (MLR) was implemented using selected intrinsic coal parameters. The results are presented in Table 5, where the developed model achieved a coefficient of determination of R² = 0.633, indicating moderate predictive capability. Despite the acceptable performance, the univariate analyses presented in the aforementioned demonstrated that the relationships between intrinsic parameters and CPT are predominantly nonlinear. This discrepancy highlights an inherent limitation of linear regression when applied to complex physicochemical processes such as coal spontaneous combustion, where multiple variables interact simultaneously. Although most regression coefficients were statistically significant (Sig. < 0.05) and no severe multicollinearity was observed (as indicated by tolerance and VIF values in Table 5), further improvement using purely linear formulations remained limited. Additional linear models with different parameter combinations yielded R² values of up to approximately 0.73. To overcome these limitations, nonlinear multiple regression was subsequently employed. Based on the relationships identified earlier, polynomial degrees of 3 were assigned to most variables, while fixed carbon was modelled using a second-degree polynomial. The resulting nonlinear regression model, summarised in Table 6, significantly improved predictive performance, achieving R² = 0.824, corresponding to an approximate 20% increase compared to the linear model. However, this improvement was accompanied by increased model complexity due to the large number of polynomial terms, which may reduce interpretability and limit practical implementation. To address dimensionality and multicollinearity concerns, principal component analysis (PCA) was integrated with regression modelling. Several PCA-based scenarios were evaluated. Linear regression using three principal components derived from the
Vitrinite
3
Liptinite
3
Inertinite
3
spontaneous combustion assessment in mining applications requires not only accurate prediction but also transparent decisionmaking. Unlike black-box models, decision trees generate explicit classification rules that can be directly interpreted by mining engineers. Therefore, the selected models provide complementary advantages, including statistical interpretability, nonlinear prediction capability, and practical applicability for mine safety decision support. After selecting the appropriate modelling approaches, different model configurations and parameter settings were evaluated through an iterative testing procedure to determine the most suitable structures and parameters for prediction. For regressionbased approaches, different mathematical formulations and model structures were examined, and the optimal formulations were selected based on the highest prediction performance and the lowest error values. In nonlinear regression analysis, different polynomial degrees were tested for the input parameters, and the final model was selected based on the best agreement between predicted and experimental CPT values. For artificial intelligence models, several network architectures with different numbers of hidden layers and neurons were developed and evaluated. The final ANN, MLP, and RBF structures reported in this study correspond to the configurations that provided the best predictive performance among the evaluated alternatives. Similarly, different decision tree structures were The Journal of the Southern African Institute of Mining and Metallurgy
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Assessment of coal spontaneous combustion potential using statistical and machine original variables resulted in R² = 0.424, while nonlinear regression based on PCA-transformed variables achieved R² = 0.763. However, applying PCA to nonlinear features resulted in a reduction in predictive accuracy compared to the full nonlinear regression model. Overall, while regression-based approaches demonstrated acceptable to good performance, their limitations in capturing complex nonlinear interactions motivated the adoption of intelligent modelling techniques.
Artificial intelligence methods Given the inherently nonlinear and multivariate nature of coal spontaneous combustion, artificial intelligence (AI) techniques were employed to improve prediction accuracy and capture complex interactions among intrinsic coal parameters.
Model configuration and hyper parameter selection
The configuration of the developed statistical and intelligent models was determined through an iterative model development and evaluation procedure. Due to the complexity of coal spontaneous combustion behaviour and the limited size of the available dataset, different model configurations were examined, and the final models were selected based on their predictive performance and generalisation capability. For artificial neural network models, different network architectures were evaluated by varying the number of hidden layers and neurons. Several combinations of network structures were trained and tested, and the architecture providing the lowest prediction error and the best agreement between predicted and experimental CPT values was selected as the final configuration. The detailed results of all tested architectures are not presented to avoid excessive repetition; however, the selected optimal structures are reported in the following sections. Similarly, for regression-based models, different mathematical formulations and parameter combinations were investigated. In nonlinear regression analysis, different polynomial orders were
evaluated for each input variable, and the final formulation was selected based on the highest coefficient of determination and the lowest prediction error. For the principal component analysis (PCA)-based models, different numbers of components were also examined to determine the optimal representation of the original variables. For the decision tree model, different splitting criteria and tree structures were evaluated, and the final model was selected based on classification accuracy while maintaining an interpretable structure suitable for practical mining applications. Therefore, the reported models represent the optimal configurations obtained after systematic evaluation of alternative structures rather than arbitrary model settings.
Hybrid linear–nonlinear artificial neural network In the first step, a hybrid linear–nonlinear artificial neural network was developed using intrinsic coal parameters as input variables. This approach combines linear transformations with nonlinear activation functions to enhance model flexibility while maintaining numerical stability. As illustrated in Figure 7, the developed model achieved a mean squared error (MSE) of 69, a root mean squared error (RMSE) of 8.3, a mean error close to zero (−2 × 10-⁶), and a---n error standard deviation of 8.33. The model was trained using different polynomial orders, and the results indicated that a configuration with polynomial order 8 yielded the lowest prediction error. The final model demonstrated a prediction error of approximately 8.3%, confirming the effectiveness of hybrid ANN structures in modelling coal spontaneous combustion potential.
Multilayer perceptron neural network In the next step, a multilayer perceptron (MLP) neural network was implemented to model the nonlinear relationships between intrinsic coal parameters and CPT values.
Figure 7—Implementation of a combined linear and nonlinear neural network method 550
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Assessment of coal spontaneous combustion potential using statistical and machine To further enhance model performance, the architecture was extended to include three hidden layers. The results of this enhanced model are presented in Figure 9, which illustrates regression plots comparing predicted and experimental CPT values for training, validation, testing, and overall datasets. As observed in Figure 9, the data points are closely distributed around the 45-degree reference line, indicating strong agreement between predicted and measured values. The correlation coefficients were calculated as 0.94 (training), 0.87 (validation), 0.96 (testing), and 0.93 (overall), demonstrating high predictive accuracy. Moreover, the consistency between training and testing results suggests that the model does not exhibit overfitting and maintains good generalisation performance when applied to unseen data.
Radial basis function neural network Figure 8—Implementing regression using the MLP neural network method
As shown in Figure 8, sensitivity analysis was conducted to determine the optimal number of neurons in the hidden layer. The results indicated that the minimum prediction error was achieved with three neurons, which was selected as the optimal configuration.
In the third step, a radial basis function (RBF) neural network was developed to further improve prediction accuracy. RBF networks are particularly effective in capturing localised nonlinear patterns due to their radial activation functions. As illustrated in Figure 10, the RBF model achieved a mean error close to zero (4.35 × 10-¹³) and an error standard deviation of 5.72, resulting in an overall prediction error of approximately 5.7%. Compared to the MLP model, the RBF network demonstrated
Figure 9—Implementing regression using a neural network method The Journal of the Southern African Institute of Mining and Metallurgy
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Assessment of coal spontaneous combustion potential using statistical and machine improved predictive performance, highlighting its superior capability in modelling complex nonlinear relationships among intrinsic coal parameters.
Decision tree model Finally, a decision tree (DT) model was developed using hierarchical classification based on intrinsic coal parameters. The model was constructed using ID3 and CART algorithms with a top-down recursive partitioning approach. The analysis identified pyrite content as the most influential parameter, which was selected as the root node with a threshold value of 0.64%. At the second level, inertinite content was identified as the dominant splitting parameter, with a threshold of 12.36%. Based on the derived decision rules: ➤ Samples with pyrite < 0.64% and inertinite > 12.36% show approximately 97% probability of low spontaneous combustion potential. ➤ Samples with pyrite > 0.64% and inertinite > 31.758% show approximately 74% probability of moderate potential. ➤ Samples with pyrite > 0.64% and inertinite < 31.758% show approximately 82% probability of high potential. The final decision tree model is presented in Figure 11, providing a transparent, interpretable, and practically applicable framework for qualitative and semi-quantitative assessment of coal spontaneous combustion potential.
Figure 10—Implementing regression using the RBF neural network method
Quantitative comparison and performance evaluation of developed models To provide an overall assessment of the developed approaches, the predictive performance of all statistical and intelligent models was quantitatively compared using mean squared error (MSE) and root mean squared error (RMSE). The results of this comparative evaluation are summarised in Table 7. This analysis enables a direct quantitative assessment of the predictive capability of each modelling approach and provides a basis for discussing their advantages and limitations in practical applications. As shown in Table 7, the multiple linear regression model exhibited the highest prediction error, indicating its limited capability in representing the complex nonlinear interactions governing coal spontaneous combustion. The nonlinear regression model improved prediction accuracy; however, its mathematical complexity limits practical implementation. Among intelligent models, the RBF neural network achieved the lowest RMSE (5.73%). The decision tree model showed a competitive prediction error (8%) while providing an interpretable classification framework, which is further elucidated later in the paper.
Model validation Model validation represents a critical step in assessing the reliability, predictive capability, and practical applicability of the developed
Figure 11—Developed model of SCCDT
Table 7 Quantitative comparison of the developed statistical and intelligent models for predicting coal spontaneous combustion potential
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No.
Modelling approach
MSE
RMSE (%)
1
Multiple linear regression (all variables)
239
15.45
2
Multiple nonlinear regression (all variables)
125
11.18
3
Hybrid linear–nonlinear artificial neural network
68.96
8.30
4
Multilayer perceptron (MLP) neural network
67.90
8.38
5
Radial basis function (RBF) neural network
32.80
5.73
6
Decision tree (DT) model
64
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Assessment of coal spontaneous combustion potential using statistical and machine predictive frameworks for coal spontaneous combustion potential. As previously discussed, the performance of artificial intelligence– based models—including hybrid ANN, MLP, and RBF—was quantitatively evaluated during the modelling phase using standard error metrics. Among all developed approaches, the decision tree (DT) model demonstrated a highly competitive predictive performance while offering a key advantage in terms of interpretability and transparency, which are essential for practical implementation in mining environments. Therefore, this model was selected for comprehensive validation. The validation framework was designed using a strict independent testing strategy. Before model development, 25 samples were randomly separated from the original dataset and completely excluded from all training, model configuration, and parameter selection procedures. These samples were used exclusively as an independent internal testing dataset to evaluate the predictive performance of the developed models. In addition, nine independent coal samples collected from the Parvadeh Tabas coal mines were used exclusively for external validation. These field samples were not involved in model training, testing, or parameter adjustment and were only used to assess the generalisation capability of the proposed model under real mining conditions. Although k-fold cross-validation and repeated cross-validation are commonly used for evaluating machine learning models, the present study adopted an independent hold-out testing strategy combined with external field validation. This approach was selected because the primary objective of this research was to evaluate the capability of the developed models in predicting spontaneous combustion potential for completely unseen coal samples and to assess their applicability under actual mining conditions. Therefore, the reserved testing dataset and independent mine samples provide a more realistic evaluation of model generalisation capability. Considering the relatively limited size of the available dataset, potential overfitting was carefully considered during model development. To minimise this risk, the independent testing samples were separated before model training and were not used for model configuration or parameter selection. Furthermore, the final model structures were selected based on their predictive performance on unseen data rather than training performance alone. The additional external validation using independent coal samples from the Parvadeh Tabas mines provided further evidence regarding the generalisation capability of the developed framework. However, the authors acknowledge that increasing the number of samples from different coalfields and geological conditions would further improve the reliability and applicability of the proposed models. The validation results were evaluated through two complementary stages: internal testing using reserved samples and external validation using independent field data. Although the developed models demonstrated satisfactory predictive performance, the uncertainty associated with model predictions should be considered when applying the framework in practical mining environments. The main sources of uncertainty include variability in coal properties, differences in experimental CPT measurements, and limitations related to the available dataset size. Therefore, the validation strategy adopted in this study, including independent testing samples and external field validation, was intended not only to evaluate prediction accuracy but also to examine the reliability of model outputs under unseen conditions. The Journal of the Southern African Institute of Mining and Metallurgy
Internal validation In the first stage, internal validation was conducted using a subset of the original dataset that had been excluded from model training. Out of the 25 coal samples reserved for testing, the decision tree model correctly classified 23 samples, with only two misclassifications, resulting in an accuracy of approximately 92%. This result confirms the robustness and generalisation capability of the proposed decision tree framework, indicating that the model can reliably predict spontaneous combustion potential for unseen data.
External validation using field data To further evaluate the practical applicability of the model, external validation was performed using independent data collected from the Parvadeh underground coal mines in the Tabas coalfield, Iran. The Tabas coalfield, located approximately 85 km southeast of Tabas in South Khorasan Province, represents one of the most significant coal reserves in Iran, with an estimated resource of approximately 1.2 billion tonnes. The Parvadeh mining complex consists of six structurally controlled zones separated by major fault systems, as illustrated in Figure 12. The spatial distribution of the main fault systems is further detailed in Figure 13, which highlights the geological complexity of the mining area. For validation purposes, intrinsic coal properties from nine samples collected from four Parvadeh mines were used. The dataset includes proximate analysis and petrographic maceral composition, as summarised in Table 8.
Figure 12—Location of the Parvadeh coal mines and major separating faults
Figure 13—Location of major fault systems in the study area VOLUME 126
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Assessment of coal spontaneous combustion potential using statistical and machine Table 8 Intrinsic parameters of nine coal samples from Parvadeh Tabas mines moisture
ash
fixed carbon (%)
pyrite
vitrinite
liptinite
inertinite
(%)
volatile matter (%)
(%)
(%)
(%)
(%)
class of tendency to spon com
(%)
S1P1C1W3
0.77
27.93
17.38
53.92
0.11
47.31
3.89
48.69
1
S2P1C1E3
2.73
36.59
14.94
45.74
0.45
36.17
8.15
55.23
1
S3P2B1B2
4.68
33.11
14.97
44.24
1.78
46.22
10.69
41.31
2
S4P2B1B3
6.56
32.3
17.65
43.5
4.52
80.23
15.25
0
3
sample code
S5P2B2B3
3.73
43.95
15.38
36.94
1.53
44.73
5.8
47.94
1
S6P3C1B1
7.94
24.38
21.11
46.56
4.66
78.97
16.37
0
3
S7P4C1B1
2.56
15.11
23.11
59.21
1.51
39.44
9.25
49.8
2
S8P4B2B4
0.87
17.59
24.49
55.99
1.06
39
14
47
2
S9P4C1B4
1.57
19.77
25.04
52.26
1.37
39
12
49
3
Table 9 Comparison between experimental and predicted spontaneous combustion classes Name
The class of tendency to spon com based on the experimental CPT value
The class of tendency to spon com based on the predicted CPT value
S1P1C1W3
weak
weak
S2P1C1E3
weak
weak
S3P2B1B2
moderate
moderate
S4P2B1B3
high
high
S5P2B2B3
weak
weak
S6P3C1B1
high
high
S7P4C1B1
moderate
moderate
S8P4B2B4
moderate
moderate
S9P4C1B4
high
high
The selected samples represent different levels of spontaneous combustion tendency (low, moderate, and high), providing a suitable basis for evaluating model performance under real mining conditions.
Model performance and comparison The comparison between experimentally determined CPT-based classes and the predicted classes obtained from the decision tree model is presented in Table 9. As shown in Table 8, the decision tree model successfully predicted the spontaneous combustion tendency class for all nine coal samples, achieving 100% prediction accuracy for the external validation dataset. This result demonstrates the strong generalisation capability of the model when applied to completely independent field data. Moreover, it confirms that the decision tree model can effectively capture the governing relationships between intrinsic coal properties and spontaneous combustion behaviour.
Practical implications Beyond its predictive accuracy, the decision tree model offers significant practical advantages. Unlike complex black-box models 554
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such as neural networks, the decision tree provides explicit decision rules that can be easily interpreted and applied by mining engineers. The ability to directly relate parameters such as pyrite and inertinite content to spontaneous combustion risk enables rapid and cost-effective preliminary assessment in underground coal mining operations.
Discussion Nonlinear behaviour of coal spontaneous combustion potential The present study demonstrated that coal spontaneous combustion potential is governed by complex nonlinear interactions among multiple intrinsic coal properties rather than by individual controlling factors. The pairwise regression analyses showed relatively low coefficients of determination (R² ≈ 0.04–0.60), indicating that no single parameter can sufficiently describe the variation in CPT values. This finding is consistent with the physicochemical nature of coal oxidation, where moisture, mineral matter, maceral composition, and reactive components simultaneously influence oxygen adsorption, heat generation, and The Journal of the Southern African Institute of Mining and Metallurgy
Assessment of coal spontaneous combustion potential using statistical and machine thermal accumulation. The limited performance of single-variable models highlights the importance of considering interactions among multiple parameters. Although individual factors such as pyrite content have strong theoretical relevance due to their catalytic effect on oxidation reactions, their influence is strongly dependent on the surrounding coal matrix and environmental conditions.
Comparison of statistical and intelligent modelling approaches The comparison of developed models indicates that different approaches provide different advantages depending on the intended application. The quantitative comparison presented in Table 7 confirms that increasing model complexity improves the capability to capture nonlinear relationships among intrinsic coal properties and spontaneous combustion potential. Classical statistical models provide valuable insight into parameter–response relationships and are useful for identifying general trends; however, their predictive capability decreases when multiple interacting variables and nonlinear mechanisms are involved. The nonlinear regression model improved prediction performance compared with linear regression, achieving an R² value of approximately 0.82. Nevertheless, the resulting high-order mathematical formulation reduced model transparency and limited its direct application in routine mine safety assessments. Intelligent models demonstrated higher flexibility in representing complex nonlinear interactions. The RBF neural network achieved the lowest prediction error (approximately 5.7%), indicating its strong approximation capability for coal oxidationrelated processes. However, similar to other neural network-based approaches, the internal relationship between input variables and final predictions remains difficult to interpret. The decision tree model provided a balance between predictive accuracy and practical applicability. Although its numerical error was slightly higher than that of the RBF model, it generated explicit classification rules that can be directly interpreted by mining engineers. Therefore, the selection of an appropriate model depends on the intended objective: RBF networks are preferable when maximum prediction accuracy is required, whereas decision tree models are advantageous for operational decision support where transparency and interpretability are essential. Physical interpretation of dominant parameters The decision tree analysis identified pyrite and inertinite contents as the dominant parameters controlling spontaneous combustion classification. This finding agrees with previous experimental observations. The selected intrinsic parameters represent different physical and chemical mechanisms involved in coal self-heating. Moisture content has a dual influence on spontaneous combustion behaviour: a certain amount of moisture can facilitate oxidation reactions and oxygen adsorption, whereas excessive moisture may consume heat through evaporation and delay temperature accumulation. Ash content generally reduces spontaneous combustion susceptibility by acting as an inert thermal component that absorbs part of the generated oxidation heat. However, the influence of ash depends on its mineral composition and interaction with reactive coal components. Volatile matter and fixed carbon contents are closely related to coal rank and chemical reactivity. Higher volatile matter generally indicates a greater amount of reactive organic compounds, which The Journal of the Southern African Institute of Mining and Metallurgy
can enhance oxygen adsorption and oxidation rates. In contrast, fixed carbon represents the relatively stable carbon fraction, and its effect on spontaneous combustion is influenced by the balance between combustible carbon content and coal structure. Petrographic components also play a fundamental role in oxidation behaviour. Vitrinite and liptinite are considered more reactive macerals due to their higher oxygen affinity and chemical activity, whereas inertinite generally exhibits lower reactivity because of its more condensed aromatic structure and reduced oxidation potential. Pyrite is one of the most important mineralogical factors affecting spontaneous combustion because its oxidation reactions are highly exothermic and can accelerate heat generation during the early stages of self-heating. Therefore, the identification of pyrite as a dominant parameter by the decision tree model is consistent with established coal oxidation mechanisms. Pyrite accelerates coal oxidation through catalytic reactions and promotes heat generation during low-temperature oxidation. However, its effect is influenced by other coal characteristics, explaining why pyrite alone cannot fully determine spontaneous combustion potential. Inertinite content also plays an important role due to its relatively low reactivity compared with vitrinite and liptinite. The interaction between reactive macerals, mineral components, and oxidation conditions explains why spontaneous combustion behaviour cannot be attributed to a single parameter. The use of proximate analysis parameters in this study was primarily motivated by their practical availability in mining operations. Although elemental analysis may provide additional chemical information, its application is often limited by data availability from operating mines. Therefore, the proposed framework provides a balance between prediction accuracy and practical accessibility.
Practical applicability of the proposed framework The practical applicability of a predictive model is a critical consideration for mine safety management. Unlike complex blackbox models that require extensive computational resources and specialised interpretation, the proposed SCCDT model provides a simple and transparent framework that can be directly implemented in underground coal mining operations. The input variables required by the model are limited to intrinsic coal characteristics obtained from routine laboratory analyses, including moisture, ash, volatile matter, fixed carbon, pyrite, and maceral composition. These parameters are commonly measured during coal quality assessment and mine characterisation programmes; therefore, the proposed approach does not require additional costly experimental procedures or advanced monitoring infrastructure. From a computational perspective, the SCCDT model has low processing requirements and can be implemented using standard computer systems without the need for high-performance computing facilities. Once the decision rules are established, the spontaneous combustion tendency of newly encountered coal seams can be rapidly classified based on their intrinsic characteristics. The proposed framework can also be integrated with mine monitoring and decision-support systems. For example, laboratorybased coal characterisation data can be periodically updated, while real-time monitoring information such as temperature, oxygen concentration, and ventilation conditions can be incorporated as VOLUME 126
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Assessment of coal spontaneous combustion potential using statistical and machine complementary information in future extended versions of the model. Such integration may enable the development of more comprehensive early-warning systems for spontaneous combustion risk management. It should be emphasised that the SCCDT model is intended as a practical screening and decision-support tool rather than a replacement for continuous mine monitoring systems. Its primary purpose is to provide an initial assessment of spontaneous combustion potential, assist engineers in identifying high-risk coal seams, and support the prioritisation of prevention and control strategies during mine planning and operation.
Limitations and future research directions Despite the promising results, several limitations should be acknowledged. First, although the compiled database of 165 coal samples represents a substantial effort, the dataset size remains limited compared with the complexity of coal spontaneous combustion phenomena. Therefore, the developed models should not be considered universally applicable under all geological and operational conditions. In addition, the relatively limited dataset size may increase the risk of overfitting, particularly for complex intelligent models with higher flexibility. To mitigate this issue, the model development process was conducted using independent testing data that were separated before training and were not involved in model configuration or parameter selection. Moreover, the final model evaluation included external validation using independent coal samples from the Parvadeh Tabas mines, providing an additional assessment of model generalisation capability. Nevertheless, further validation using larger datasets covering diverse coal types and geological conditions is recommended to ensure broader applicability of the developed framework. Second, the external validation dataset consisted of only nine samples from the Parvadeh coal mines. Although the proposed model achieved 100% classification accuracy for these samples, this result should be interpreted cautiously and does not indicate perfect prediction capability. Increasing the number of independent field samples from different mining regions is necessary to provide more comprehensive validation and to evaluate the transferability of the developed framework. Third, uncertainties may arise from variations in coal properties, experimental CPT measurements, geological conditions, and mining practices. These uncertainties may influence both the input space and the resulting classification boundaries of datadriven models. Therefore, the predicted spontaneous combustion classes should be interpreted as risk estimations rather than absolute deterministic outcomes. Future studies should therefore incorporate uncertainty quantification approaches, probabilistic modelling techniques, and uncertainty-aware machine learning frameworks to estimate prediction confidence and improve the reliability of risk assessments. Future research should also focus on expanding the database with additional coal types and mining environments, integrating real-time monitoring data from sensor-based systems, and developing hybrid modelling strategies that combine machine learning techniques with mechanistic understanding of coal oxidation processes. Such approaches may further enhance prediction accuracy, reduce uncertainty, and improve practical applicability in underground coal mine safety management. 556
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Conclusions Coal spontaneous combustion is a complex and nonlinear phenomenon influenced by the combined effects of intrinsic coal properties, geological conditions, and mining-related factors. Among these, intrinsic coal characteristics represent the primary intrinsic factors controlling coal oxidation and self-heating behaviour. In this study, a comprehensive data-driven framework was developed to evaluate and predict coal spontaneous combustion potential using a database consisting of 165 coal samples compiled from published experimental studies and CPT measurements. A systematic comparison of classical statistical and intelligent modelling approaches demonstrated that the relationship between intrinsic coal properties and spontaneous combustion potential is predominantly nonlinear and multivariate. Univariate and linear regression models showed limited predictive capability, confirming that individual parameters cannot adequately represent the coupled interactions governing coal self-heating processes. Although nonlinear regression and neural network-based models improved prediction accuracy, their mathematical complexity or limited interpretability may restrict their direct application in practical mine safety management. Among the developed approaches, the proposed spontaneous combustion classification decision tree (SCCDT) model provided an effective balance between predictive performance, simplicity, and interpretability. The model achieved an overall testing error of approximately 8% using 25 independent test samples that were completely excluded from the training process. Furthermore, external validation using independent coal samples from the Parvadeh Tabas coal mines achieved correct classification for all nine samples, demonstrating the applicability of the proposed framework for independent field data. The scientific contribution of this research is not based on proposing a new machine learning algorithm; rather, it lies in developing an integrated and practical framework that combines comprehensive data compilation, comparative evaluation of multiple statistical and intelligent modelling approaches, and interpretable prediction for mining applications. The SCCDT model identified pyrite and inertinite contents as dominant parameters influencing spontaneous combustion classification, which is consistent with the established physicochemical mechanisms of coal oxidation. The transparent structure of the model provides an important advantage over black-box approaches and enhances its suitability for preliminary risk assessment and decision-support in underground coal mining operations. Despite the promising results, several limitations should be acknowledged. Although the database compiled in this study represents a substantial effort, the number of available samples remains limited compared with the complexity and variability of coal spontaneous combustion phenomena. Therefore, the developed models should be considered practical predictive tools within the range of coal characteristics represented in the dataset rather than universally applicable models for all geological and mining conditions. In addition, external validation was conducted using nine coal samples from the Parvadeh Tabas coal mines, representing a specific regional geological setting. Although these samples confirmed the applicability of the SCCDT model, additional validation using larger datasets from different coalfields is required to further evaluate model generalisation and minimise potential regional bias. Future studies should focus on expanding the database, incorporating uncertainty quantification methods, The Journal of the Southern African Institute of Mining and Metallurgy
Assessment of coal spontaneous combustion potential using statistical and machine developing hybrid physics-informed machine learning approaches, and integrating real-time monitoring data to enhance the reliability and operational applicability of spontaneous combustion prediction systems. Overall, this study demonstrates that combining statistical analysis with interpretable intelligent modelling provides a practical and reliable framework for assessing coal spontaneous combustion potential. The proposed SCCDT approach can serve as an effective decision-support tool during mine planning, operation, and safety management, while providing a foundation for future hybrid and uncertainty-aware predictive models.
Gbadamosi, A.R., Onifade, M., Genc, B., Rupprecht, S. 2021. Spontaneous combustion liability indices of coal. Combustion Science and Technology, vol. 193, no. 15, pp.2659–2671. Guney, M. 1968. Oxidation and spontaneous combustion of coal: Review of individual factors. Colliery Guardian, vol. 216, no. 105-110, pp.137–143. Hodges, D.J. 1963. The influence of moisture on the spontaneous heating of coal (Doctoral dissertation, University of Nottingham).
Data availability
Kaymakci, E., Didari, V. 2002. Relations between coal properties and spontaneous combustion parameters. Turkish Journal of Engineering and Environmental Sciences, vol. 26, no.1, pp.59–64.
The datasets generated and/or analysed during the current study are not publicly available because they are part of ongoing research and may be used in future publications. However, the data are available from the corresponding author upon reasonable request.
Kursunoglu, N., Gogebakan, M. 2022. Prediction of spontaneous coal combustion tendency using multinomial logistic regression. International Journal of Occupational Safety and Ergonomics, vol. 28, no. 4, pp.2000–2009.
Conflict of interest
Lang, L., Fu-Bao, Z. 2010. A comprehensive hazard evaluation system for spontaneous combustion of coal in underground mining. International journal of coal geology, vol. 82, no. 1-2, pp.27–36.
On behalf of all authors, the corresponding author states that there is no conflict of interest.
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The Journal of the Southern African Institute of Mining and Metallurgy
A critical appraisal of the deformationbased support design approach by J. Wesseloo
Affiliation: 1 Australian Centre for Geomechanics, The University of Western Australia Correspondence to: J. Wesseloo Email: info-acg@uwa.edu.au
Dates:
Received: 01 May. 2026 Revised: .21 Jul. 2026 Accepted: 14 Aug. 2026 Published: Sep. 2026 How to cite: Wesseloo J. 2026. A critical appraisal of the deformation-based support design approach. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 9, pp. 559 – 572
Abstract
Empirical approaches, numerical modelling, and analytical methods are employed in ground support design for rockburst conditions, but only analytical approaches attempt to quantify the safety margins of a design. The Canadian Rockburst Handbook approach is the most widely known analytical approach. Stacey (2016) identified a state of ‘design indeterminacy’, arguing that neither demand nor capacity can be determined with sufficient accuracy for conventional design processes. Since then, several important advancements were made with the development of the deformation-based support design approach. An independent appraisal of the DBSD approach is lacking. This paper provides an independent appraisal of the deformationbased support design method, summarising the approach, identifying implicit and explicit assumptions, highlighting uncertainties, and raising questions requiring resolution. The resolution of the identified issues is necessary to demonstrate that the deformation-based support design methodology – when applied as a design tool – satisfies the standards expected of a robust engineering approach.
Keywords Deformation-based support design, DBSD, dynamic ground support design
Introduction DOI ID: https://doi.org/10.17159/2411-9717/3976/2026 ORCiD: J. Wesseloo https://orcid.org/0000-0001-7124-2267
Approaches to ground support design in general, and more specifically for dynamic conditions, are quite varied in methodology and sophistication. Although numerical modelling can be used to model specific instances of dynamic ground displacement and the interaction with ground support elements (e.g., Lilley et al., 2013), Stacey (2016) concluded that it is not practical for rock support design under dynamic loading conditions. Empirical methods, on the other hand, provide guidance in selecting appropriate support for different conditions. Many approaches exist with several being site-specific (Graham et al., 2023; Mikula, Gebremedhin, 2017; Mikula, 2013; Counter, 2017 in Potvin, Hadjigeorgiou 2020). Being based on historical rockburst damage data, these approaches seldom include newer products or systems and do not provide any way to quantify the safety margin. Analytical methods aim to quantify the demand on, and the capacity of, the support system and with that quantify the safety margin associated with a support system under the chosen design conditions. The most well-known analytical support design approach was developed as part of the Canadian Rockburst Research Programme performed in the 1990s (Mining Research Directorate, 1996) and is often referred to as the Canadian Rockburst Handbook (CRH) method. Often informally discussed, but seldom officially acknowledged, is the fact that analytical methods are sometimes used with carefully selected input parameters for the purpose of satisfying the requirement for a design justification, while the selection of support is done with empirical methods or with a process of evolution – upgrading components or systems when current systems start to underperform. Kaiser and Cai (2013) critically evaluated the design assumptions underpinning several support design approaches and concluded that ‘it is time to rethink’. In 2016, Stacey concluded that design of rock support to contain rockburst damage cannot be carried out using a conventional design process as a state of ‘design indeterminacy’ exists, since neither the demand on the support system, nor the capacity of the support system, can be sufficiently accurately determined.
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A critical appraisal of the deformation-based support design approach
General framework The performance of support components and support systems are often expressed as force versus displacement, as shown in Figure 1a. The energy consumed by the support system is obtained through the integration of the force–displacement curve, i.e., the area under
Peak load capacity, Lpeak = FCS(dpeak) Support system energy consumption ECS(d)
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The deformation-based support design (DBSD) is collectively contained in several publications. These are Kaiser and Moss (2022), Kaiser and Malovichko (2022), and Rigby et al. (2026), all of which present and discuss the approach in design, and Moss and Kaiser (2022), which presents and discusses the approach in the form of preventative support maintenance. Malovichko (2023) and Primadiansyah et al. (2025) discuss the implementation of the
Residual energy capacity, RECS(d) d
a)
Deformation-based support design Summary
Dynamic force-displacement response of support system, FCS(d)
RECS(δ)
dpeak
Surface displacement, d
Support system energy consumption, ECS(d) ECS(δ)
δ
b)
Failure at dult
dult ECS(δ)
Energy
The Canadian Rockburst Handbook (CRH) developed a comprehensive and systematic analytical approach that distinguished between three different loading mechanisms with different design assumptions and acceptance criteria for each of these. These three loading mechanism are (Mining Research Directorate, 1996): ➤ strainburst ➤ strainburst triggered by remote event ➤ shakedown. Self-initiating rockbursts and dynamic loading from remote events share a similar framework, whilst the approach to shakedown loading differs considerably from them. The methodology for strainburst and triggered strainburst mechanisms can be summarised as follows: ➤ Assess the depth of dynamic failure and the mass to be mobilised. ➤ Assess the velocity of movement of failing rock mass. ➤ Assess the kinetic energy of the mobilised rock mass (demand) and compare it with the energy absorption capacity available within the support system. The CRH equated the velocity of movement of the rock mass to an ejection velocity, which was obtained from the peak ground velocity (PGV) and an uncertain amplification factor. The link between PGV and ejection velocity appear tenuous (Kaiser, Cai 2013). The unknown amplification factor contributed to the design indeterminacy that Stacey (2016) refers to. Kaiser and Moss (2022) point out that the CRH approach assumes the full installed capacity of the support to be available for arresting the rockburst loading and points out that this is not the case, as some of the support capacity is consumed through quasistatic rock mass deformation. These two major shortcomings of the CRH are addressed in the DBSD approach.
Installed capacity, RECS(0)
The Canadian Rockburst Handbook approach
approach and the selection of input parameters. The approach to assessing the support system capacity used in the DBSD literature was first presented by Cai and Kaiser (2018). The DBSD approach addresses the three loading mechanisms mentioned in the previous section, making important adaptions to the first two mechanisms and maintain the original approach from the CRH for the shakedown mechanism. The DBSD and CRH approaches share the same methodology as listed in the previous section. In the DBSD, however, the velocity of movement of the rock mass is linked to the bulking velocity rather than an ejection velocity, which is a conceptually more defensible approach. Another important conceptual change in the DBSD approach is its focus on the residual, rather than installed, capacity. The DBSD approach also provides a conceptual framework that, together with its graphical representation, facilitates discussion on the functioning of support systems under dynamic loading. From the DBSD literature, I found the approach conceptually easy to understand but difficult and somewhat esoteric when attempting its implementation. The following sections present the DBSD as I understand it from the literature. This may provide readers with a useful introduction to the DBSD and will allow the authors of the DBSD approach to correct any misconceptions I might have, and which others might share. With these sections of the paper, I aim to contribute to making the method more transparent and interpretable. Several assumptions are made in the DBSD approach, not all of which are explicitly stated. The qualification of the assumptions and the quantification of uncertainties related to them are not always clear. In this paper, the implicit and explicit assumptions made in the DBSD are highlighted to facilitate further discussion and analysis.
Force
Since Stacey (2016) came to that conclusion, some important contributions have been made to analytical support design in the form of the deformation-based support design (DBSD) approach collectively contained in the DBSD literature, which is discussed further on in this paper. Since its early development, the DBSD has increasingly been implemented at mine sites with examples reported in literature (Lowther et al., 2022; Primadiansyah et al., 2025). It is important to recognise the improvements made with the DBSD approach as well as understand its assumptions and shortcomings. An independent evaluation and appraisal of the DBSD approach is, however, lacking. Rigby et al. (2026) points out that the design approach needs to be transparent and interpretable. This paper provides an appraisal to contribute to this end.
RECS(δ) δ
Surface displ., d
Residual energy capacity of the support system, RECS(d)
Surface displ., d
c)
Figure 1—Conceptual illustration of consumed energy and residual energy capacity of the support system The Journal of the Southern African Institute of Mining and Metallurgy
A critical appraisal of the deformation-based support design approach the curve, and is generally displayed as shown in Figure 1b. Over time, excavations will deform under quasistatic stress changes and dynamic loading, and some of the capacity of the support system will be consumed. For this reason, the DBSD focuses on the residual support system energy capacity (RECS) rather than the total installed capacity (RECS(0) = ECS(dult)), as presented in Figure 1b. At any surface displacement value, d, the remaining capacity of the support system is given by the RECS(d) function. Total energy capacity at installation, RECS(0)
ΔEh
Energy
Residual energy capacity at start of dynamic loading, RECS(dh) RECS(d) Total energy demand, ED Energy demand on support system due to seismic event, DP(d-dh)
dh
Total displacement demand, dD
dtotal dult
Surface displ., d
Energy
Figure 2—Deformation-based support design representation of energy demand on, and energy capacity of, the support system as a function of surface displacement
1) RECS(d)
DP(d d)
4) ED
2) d 0 3) dD Surface displacement, d Figure 3—Components of the deformation-based support design framework that need to be quantified for use in design and preventative support maintenance
In the DBSD literature, the preferred way of graphically representing the RECS(d) is shown in Figure 1c, which is the inverse representation of Figure 1b. Figure 2 provides the graphical representation of the DBSD framework that is generally used in the DBSD approach. In addition to the RECS, Figure 2 also shows what DBSD refers to as the ‘demand path’, represented as a double line. The demand path represents the kinetic energy demand that is placed on the support system due to dynamic displacement of the rock mass (Kaiser, Moss, 2022; Malovichko, 2023). The DBSD highlights that since some deformation will occur before the system is loaded dynamically, the energy demand function should not start at the origin but at the total historic displacement, dh. Due to dh, the residual energy capacity of the support system is less than the total available capacity at installation, as illustrated in Figure 2, with ΔEh of the capacity being consumed through dh. An important feature of the DBSD approach is the fact that with both the energy capacity and the energy demand expressed as functions of displacement, one can evaluate stability in terms of the displacement imposed on the support system. Within the DBSD framework the historic mode of displacement (quasistatic or dynamic) is not important in the assessment of RECS (Moss, Kaiser, 2022). Since the total displacement imposed on the support system can be measured, the residual capacity of the support system can, at any time, be assessed and evaluated against expected future dynamic demand. In this way, the DBSD approach provides a framework for both design and preventative support maintenance (Moss, Kaiser, 2022). The four components that need to be quantified to use the DBSD methodology for support design and preventative support maintenance are listed in the following and are shown in Figure 3: ➤ The residual energy capacity of the support system as a function of displacement, RECS(d). ➤ The historical displacement, dh, before the design dynamic loading is imposed. ➤ The total dynamic displacement demand, dD. ➤ The total kinetic energy demand, ED. Components one and two quantify the residual energy capacity of the support system available to arrest a design dynamic event. Components three and four quantify the dynamic energy demand. Within the current DBSD framework, failure is defined as the point of intersection between residual capacity (RECS(d)) and the demand path, (DP(d)) or extension thereof, as illustrated in Figure 4.
Estimation of support system capacity Cai and Kaiser (2018) proposed the estimation of the design capacity of a support system as the superposition of the average support pressure of individual components with displacement. They point out that during a rockburst, the energy is absorbed by the entire system, which includes the energy absorbed due to deformation of the bolts at the plates (direct loading), ECbd, the internal loading of the support, ECbi, the areal support, ECAS , and the energy consumed through the breaking and deformation of the rock mass, ECrock, i.e.,: [1] where ECS is the energy absorption capacity of the support system.
Figure 4—Definition of failure in current formulations of the deformationbased support design (Malovichko, 2023) The Journal of the Southern African Institute of Mining and Metallurgy
The summation indicates that the capacity is the result of different types of surface support and reinforcement elements. Cai and Kaiser (2018) argue that under strainbursting VOLUME 126
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A critical appraisal of the deformation-based support design approach conditions only a small part of the energy released from the ground surrounding the burst volume is dissipated by the failing rock and that ECrock may be assumed to be zero. They also point out that under tangential straining conditions one would not expect this to be the case. Assuming ECrock=0 is, however, a conservative assumption in all cases. By assuming a constant direct/indirect loading split, Equation 1 is simplified to:
with the number of units of that type per square meter. Both areal support and reinforcement elements are then expressed as a support pressure (kN/m2). ➤ Assume a level of load splitting, 0≤κ≤ 1 and adjust displacement values for load splitting by multiplying displacement values with (1+κ). ➤ Offset the load–deformation curve for any element with deformation that may have occurred in the drive before that particular element was installed. ➤ Construct the system energy absorption capacity, ECS(d), of the support system as the superposition of the individual curves. The residual energy capacity of the support system is obtained as RECS(d) = ECS(dult)–ECS(d). An example application of the evaluation of the support system capacity using this approach is presented in Figure 5, based on the support system specifications listed in Table 1. The values in Table 1 are obtained from the example presented by Kaiser and Moss (2022). When superimposing the energy capacity, care needs to be taken to represent physically possible behaviour. Kaiser and Moss (2022) point out the energy consumption capacity of the surface support can only be mobilised as long as it is anchored by tendons. However, it should be noted that this capacity is not lost at the point of bolt failure but is never available and should not be included in the capacity prior to the bolt failure (Figure 6). Figure 5e shows a total energy absorption capacity of the areal support of 14 kJ/m2 at a total displacement of 250 mm (Kaiser and Moss (2022) Figure 9). The surface support functions only as part of the system and cannot mobilise its residual capacity beyond the breaking point of the bolts at about 163 mm. The total available energy capacity from the areal support is thus limited to about 9 kJ/m2. When combining the energy capacity of the different elements of the support system, an assumption needs to be made about the relative deformation of the support elements. The relative displacement between reinforcement elements and the areal support is illustrated in Figure 7. Figure 7a illustrates a deformation profile as a result of a rockburst, with the bolt head displacement denoted as db and the maximum displacement of the areal support as dAS. One can write the dAS as a multiplier of db, i.e.,:
[2] where, ECbd = e nergy absorption through direct loading (plate loading) ECs = energy absorption of the support system κ =w ork split ratio between direct and indirect loading = . There seems to be no justification for assuming a constant load split value over time, or between different reinforcement element types. One would expect different values of κ for different rockburst magnitudes and depths. It is not clear what the impact of this assumption is, and currently, there appears to be no way to evaluate this assumption. Load splitting values of κ=1 indicate conditions where only internal loading occurs, with the internal loading capacity being assumed to be twice that of the direct loading. A κ=0 indicates that only direct loading occurs through the plate connection. For debonded tendons, the two modes are the same and κ=0 should be used. Cai and Kaiser (2022) highlights the importance of considering the ratio of energy absorption between single-tube and split-tube tests when selecting ground support elements and provides typical ratios for different type of bolts. The single- and split-tube tests are, however, laboratory index tests and design values for κ cannot, with any confidence, be based on these laboratory values. There appears to be no other way of estimating the value. The methodology for estimating the RECS(d) proposed by Cai and Kaiser (2018), and used within the DBSD approach (Kaiser, Moss, 2022; Moss, Kaiser, 2022; Primadiansyah et al., 2025), can be summarised as follows: ➤ Estimate the load–deformation capacity of each of the elements in the support system. Load for reinforcement element will be expressed as force (kN) and for areal support as force per unit area (kN/m2). ➤ Normalise each of the element contributions as a square unit contribution by multiplying the force from (step 1)
[3] where f is a factor > 0. The value of f is related to the relative proportion of energy
Table 1 Specifications for support system as presented by Kaiser and Moss (2022) Reinforcement Name
Areal support Cable bolt
Debonded bolt
Name
Mesh reinforced shotcrete
Yield load (kN)
146
146
Yield load (kPa)
55
Spacing (m × m)
1.3 × 1.3
1.3 × 1.3
dyield#(mm)
0
110
40
dult*(mm)
250
Pre-install displacement (mm)
25
50
Pre-install displacement (mm)
0
Load split, κ-value
0.25
0
dult*(mm)
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A critical appraisal of the deformation-based support design approach Pressure (kN/m²)
150 100 50 0 0
50
100 150 200 Displacement (mm )
Cable bolt
250
300
50
100 150 200 Displacement (mm )
250
300
AS
d) Areal support pressure capacity 15
200 150
Energy Capacity (kJ/m²)
Force (kN/m ²)
0
Debonded bolt
a) Force-displacement curves for individual reinforcement elements
10
100 50 0 0
50
100 150 200 Displacement (mm )
250
Debonded bolt
Total Bolts
Cable bolt
300
5 0 0
50
100 150 200 Displacement (mm ) AS
b) Support pressure capacity of reinforcement elements
250
300
AS Total available
e) Areal support energy capacity 30.0
20 15
Energy capacity (kJ/m²)
60 50 40 30 20 10 0
Energy Capacity (kN/m ²)
Force (kN)
200
20.0
10
10.0
5 0 0
50
Cable bolt
100 150 200 Displacement (mm )
250
Debonded bolt
Total Bolts
300
c) Energy absorption capacity of reinforcement elements
0.0 0.0
50.0
100.0 150.0 200.0 250.0 300.0 Displacement (mm ) ECs
RECs
f) Combined support system energy capacity
Residual Energy Capacity (kJ/m²)
Figure 5—Assessment of the energy absorption capacity (ECS) and residual energy capacity of the support system (RECS) for a support system with components listed in Table 1
16.0 Areal support capacity not available due to bolt failure
14.0 12.0 10.0
Bolt failure occurs
8.0
r su esid pp ua or l a t c re ap al ac ity
6.0 4.0 2.0 0.0 0
50
100
x 150
200
250
Displacement (mm) AS
Tot. avl. AS
Figure 6—Residual capacity of the areal support (AS) in accounting for premature bolt failure
consumed by the bolts and the surface support. Cai and Kaiser (2018) discuss the split of energy consumption between the tendons and the surface support and state that the energy partition between reinforcement and surface support can be highly variable as it depends on the dynamic failure process and associated displacement pattern imposed on the different components. Based on the work by Bucher et al. (2013) and Ortlepp and Erasmus The Journal of the Southern African Institute of Mining and Metallurgy
Figure 7—Illustration of deformation profile (Potvin et al., 2010)
(2002), they suggest practical ranges of the energy split to be between 25:75 and 75:25 and conclude that even with more case studies, it would likely be impossible to give more specific values. In the DBSD literature, the energy split is not directly considered and is a result of the chosen value of f, with the assumption, generally made, that f = 1 (D = 2d). This assumption is convenient as it allows the plotting and superposition of the areal support and the reinforcement elements on the same displacement axis without the need for any adjustment. No justification, however, is given for this assumption and there currently appears to be no defensible way to choose one value over another. VOLUME 126
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A critical appraisal of the deformation-based support design approach The implicit assumption also made is that different reinforcement elements of different types are all subjected to the same deformation during a rockburst event, acting in perfect unison similar to the uniform piston model of the assumed loading regime, as illustrated in Figure 10. The support reaction forces acting spatially at different locations are converted to a uniform support pressure, effectively smearing the reinforcement element point loads uniformly over the supported wall, irrespective of the relative placement of support elements. To evaluate the validity of such an approach, this paper introduces the concept of representative support pattern, similar to the concept of a representative elementary volume (REV), the volume above which a rock mass can reasonably be described as a continuum (Cundall et al., 2008; Esmaieli et al., 2010; Hudson, Harrison 1997; Schultz, 1996). Different approaches to estimate REV lead to ranges of 10 to 30 times the mean block size (Cundall et al., 2008). Esmaieli et al. (2010) performed an in-depth study for a massive sulphide rock mass at the Brunswick Mine and estimated a mechanical REV of about 10 m × 10 m × 20 m. Similarly, the scale at which the support system may reasonably be represented as a uniform pressure will be several times greater than the smallest representative support pattern (SRSP). The SRSP can be described as the smallest area forming a pattern which, if repeated in a tessellation, will represent the full support pattern over a large area (Figure 8). For the approach of describing the support capacity as a combined support pressure obtained from the different components to be valid, the loading mechanism needs to be uniform over an area several times larger than the SRSP. Rockburst damage investigations, however, often indicate a nonuniform and complex deformation profile, as illustrated in Figure 9. Support elements often fail in different modes in the same rockburst area – naked tendons with plates attached often occur together with goose-necked bolts and others that failed in shear. Cai and Kaiser (2018) point out that this approach described in this section to assess the support system capacity assumes that no weak links exist and that all connections survive the dynamic impact load. They also highlight the assumption that all the tendons and the surface support energy capacities are simultaneously mobilised. An installed support system, however, consists of several elements with different stiffness installed over a rough blast profile
with variable installation qualities, subjected to varying loading by an anisotropic discontinuous rock mass. It is difficult to envisage these assumed conditions for an installed support system. Matching the strength of the different units to mitigate against premature failure of any component is, of course, a prerequisite for a defensible design and will lead to a better performing system, as highlighted by the DBSD approach. The point, however, remains that the simple uniform piston loading model does not adequately represent the true loading regime and the simple superposition of energy absorption components within a uniform piston loading framework will likely overestimate the capacity of the system by ignoring both the spatio-temporal load and strength variation.
Figure 8—Support pattern used as an underground mine with the smallest representative support pattern shown
Figure 9—Support system damaged by a seismically triggered strainburst causing loading by rock mass bulking (Kaiser, Cai 2013)
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Estimation of the displacement–energy demand Although the equations used for energy demand calculations in the CRH and DBSD are similar, there is an important conceptual difference. The CRH assumes a starting velocity equal to the ejection velocity. The rock mass decelerates due to the effect of the ground support, and the energy demand is assumed to be the total kinetic energy associated with the deceleration to zero velocity. The demand path and total energy demand in the DBSD represent the kinetic energy of the moving rock mass during the rockburst process without any support constraint and the total energy demand. The kinetic energy that needs to be absorbed by the support system, ED, is the kinetic energy of an unrestrained rock mass at the end of the bursting process. The assessment of the deformation–energy demand, that is, the demand path (DP) is no trivial exercise. Kaiser and Malovichko (2022) and Kaiser and Moss (2022) have proposed a method for assessing a demand path for design purposes. They propose an estimation of the total energy, ED, and total dynamic displacement demand, dD, for a given design rockburst assuming no ground support. ED is dependent on the bulking velocity, which, in turn, is dependent on the depth of failure and the bulking factor (BF). Kaiser and Moss (2022) and Kaiser and Malovichko (2022) use the conceptual model presented in Figure 10 and propose the following
The Journal of the Southern African Institute of Mining and Metallurgy
A critical appraisal of the deformation-based support design approach estimate of the dD, and bulking velocity, vb: [4] where: dsb = BF =
the strainburst depth of failure the bulking factor. [5]
where: dD = the dynamic displacement demand Δtsb = the time of the strainburst bulking. The conceptual model underlying the formulations in Equations 4 and 5 is illustrated in Figure 10. The conceptual model (Figure 10) considers tow rock volumes, the first being the volume of rock in the skin of the excavation and fractured before the rockburst occurs. This rock volume acts as a burden to the violently expand rock behind it. The second volume, behind the burden, is the failing rock – the source of the rockburst. The expanding rock mass is assumed to act as a piston pushing the burden at vb. The equations presented in the DBSD approach for the assessment of the ED implicitly assume a constant acceleration and an associated linear change between start and end velocities. Assuming a constant acceleration, the energy required to bring the burden to rest, DEb, is:
Assuming a linear distribution from the depth of the rockburst to the surface, the minimum, maximum, and mean BFs will be 0, 2BF and BF, respectively. Equation 5 assumes the failure depth, dsb, and the bulking rate to be constant over the bulking time Δtsb. No data is available to either support or defeat this assumption.
Parameters necessary to estimate the energy demand Cai and Kaiser (2018) point out that the actual volume increase varies considerably with BFs up to 25% for unconfined highly fractured rock, and smaller values (5% – 10%) for rock that is confined and remains in place even after being fractured. As the BF is dependent on the support performance, an assumption on the support performance is required to estimate demand on the ground support (ED and dD). Due to the assumption made on the BF, the dD includes the presence of ground support – the associated energy consumption is, however, ignored (a point discussed in more detail further on in this paper). The BF is an input parameter and presupposed, with a user required to choose a lower BF when a support system is expected to perform well. As lower BFs lead to lower displacement and energy demands, the expected good performance of the system becomes a self-fulfilling prophecy.
Conceptual Geometry At initiation
Fractured rock burden with depth, df0
[6] where: mb = the mass of the burden vb = the bulking velocity. The bulking velocity at the deepest edge of the expanding mass is zero and equal to vb at the edge closest to the surface in contact with the burden. Assuming a linear velocity profile and a constant acceleration, the total kinetic energy of the burden, DEsb, is:
After strainburst
dsb
df0
dsb
dd = dsb·BF df0
[7] where msb is the mass of the strainbursting rock. The total amount of kinetic energy demand is estimated as: [8] Rock failing and bulking during strainburst
The total kinetic energy demand estimate for this strainburst is similar to that of a self-initiated strainburst with the additional energy transferred from the remote event. ED=[Strainburst energy demand]+[energy transfered from remote event]
Velocity profiles [9]
where: n = an amplification factor PGV = the peak particle velocity from the remote events acting on the bursting rock mass. One would expect the bulking to vary from zero somewhere behind the back of the rockburst to a maximum value closer to the wall. BF in Equation 4 is therefore a representative or characteristic value representing the varying bulking within the rock mass. The Journal of the Southern African Institute of Mining and Metallurgy
Self-initiating rockburst v = vb v=0 Remote triggered rockburst v = vb v = n·PGV v=0
Portion from bulking
Portion from remote event
Figure 10—Conceptual model for estimation of displacement demand and bulking velocity (modified after Kaiser and Moss, 2022) VOLUME 126
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Stability assessment with the deformation-based support design approach With analytical design approaches, some design acceptance criteria (DAC) are defined, with the design accepted when the DAC has a sufficient margin of safety appropriate to the level of uncertainty and consequence associated with the design. Common DACs are factor of safety (FS) and safety margin (SM). FS is more commonly used in engineering design calculations, but SM is more easily represented graphically. Similar to Kaiser and Moss (2022), this paper uses FS as the DAC and uses SM in graphical representation. These two measures are related as follows: 566
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[10] [11] The capacity and demand factors in the DAC can be defined in different ways and with respect to different design parameters. Within the current DBSD (e.g., Kaiser, Malovichko, 2022; Kaiser, Moss, 2022; Malovichko, 2023; Primadiansyah et al., 2025), failure is defined as the point of intersection between residual capacity, RECS(d) and the demand path, DP(d) or extension thereof, as illustrated in Figure 4. Kaiser and Moss (2022) proposed the use of two residual FSs that need to be evaluated for the acceptance of a design. These two FSR measures are defined in terms of deformation and energy. [12] [13] Kaiser and Moss (2022) present the corresponding SMs in their Figure 14 reproduced here in Figure 13. The figure illustrates two SM definitions (green) quantifying the proximity of the RECS to the
Figure 11—Estimated depth and duration of seismic events recorded in underground mines (modified after Malovichko, 2023) 1 0.9 0.8
Cumulative probability
The duration of the bulking process can be greater than the rupture time, Δtsb ≥ tr. Malovichko (2023) states that under the assumption that the fracturing and bulking of rock during the strainburst is contained and that the bulking rock remains mechanically coupled with the surrounding rock mass, then the duration of seismic radiation and duration of bulking will be approximately the same. This may not be the case when severe rockburst damage occurs, but is reasonable as a design assumption where severe rockburst damage is to be avoided. In all cases, however, this assumption will be conservative. With the lack of direct measurements, Kaiser and Malovichko (2022) and Malovichko (2023) present back-calculated estimates of both Δtsb and dsb, as shown in Figure 11. The values of Δtsb are assumed to be equal to the duration of the source. The duration of the source is assumed to be twice the difference between the origin time calculated from the P- and S-wave first arrivals, and the centroid time evaluated from a source–time function inversion, constrained by the moment tensor (Malovichko, 2022). The values of dsb are calculated from the moment using a source model for crush type events in the skin of a tunnel, developed by Malovichko and Rigby (2022). For this back-calculation, they assumed the length of the rockburst source along the tunnel, L3, to be 15 times the dsb and be at least 3.5 m, and that there was no influence of local geology (Malovichko, 2023). Kaiser and Malovichko (2022) recommend the use of Δtsb between 20 ms and 50 ms and dsb between 0.5 m and 1 m for depth of failure. These boundaries are shown as a box in Figure 11. Since smaller Δtsb and greater depth result in higher demand, Δtsb = 20 ms and dsb = 1 m may be optimistic. With both the axes on the chart being logarithmic and the energy associated with a moving mass being proportional to the square of the bulking velocity, small changes in the proposed ranges have a large influence on the estimated energy demand. This is illustrated in Figure 12, which plots the cumulative distribution of the kinetic energy demand based on three different ranges. The blue and orange distributions assume a bulking factor of 5% and 10%, respectively, along with the ranges suggested by Kaiser and Malovichko (2022) (blue box in Figure 11). The green distribution assumes an upper limit for dsb of 0.5 m deeper and assumes the range of Δtsb = 0.01 to 0.02 ms (purple box in Figure 11). For the purpose of generating Figure 12, a uniform distribution was assumed for BF. For both Δtsb and dsb uniform distribution was assumed on logarithmic scale, i.e., data is assumed to be uniformly distributed through the blue or purple boxes. Currently, a high degree of uncertainty exists on the estimation of design parameters for strainburst depth and duration.
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
0
20
BF =5% Δt sb =20 -50ms d sb =0.5-1m
60
80
Energy demand
40
[kJ/m ²]
BF =10% Δt sb =20 -50ms d sb =0.5-1m
100
BF =5%-10% Δt sb =10 -50ms d sb =0.5-1.5m
Figure 12—Cumulative probability distribution of kinetic energy demand estimates based on different parameter range assumptions The Journal of the Southern African Institute of Mining and Metallurgy
A critical appraisal of the deformation-based support design approach ultimate displacement and energy demand (blue circle) resulting from the rockburst loading path (blue). They calculate the following FSR: [14] [15] Kaiser and Moss (2022) point out that the loading path is inclined (light blue) and suggests that the most realistic FSR is obtained with the SM defined along the loading path trajectory. With this definition, the two previously-defined FSR values are conditional on the load path. The conditional FSRs, for the example shown in Figure 13b, are calculated as follows: [16] [17] These load–path conditional FSRs appear to be the preferred FS definition within in the DBSD methodology. In a more recent publication Rigby et al. (2026) focused on the displacement safety factor, FSRd as expressed in Equation 12 . Rigby et al. (2026) argue for the use of this definition, based on the fact that the energy demand cannot be measured and the remnant support capacity is controlled by the imposed displacement. Since the energy and displacement are inextricably linked in both the demand and
residual capacity estimation, and the displacement residual capacity is defined in energy–displacement space, this argument is not convincing. It appears that the reasoning behind designating the crossing point as failure is the fact that beyond this point, the calculated energy demand exceeds the residual capacity at the same displacement. Ground support will, however, restrain the accelerating mass as soon as the bolts are mobilised and the acceleration of the rock mass will be reduced by the reaction force imparted by the support system and the energy absorbed already during the bursting period. This is illustrated in Figure 14, where the total support energy consumed during the bursting displacement is shown in orange as ΔRECS. In this illustration, the ΔRECS is greater than the total ED and is not accounted for in the DBSD approach. Figure 14b illustrates the scenario where the displacementenergy demand falls above the RECS curve. According to the definitions of SMs illustrated in Figure 13, SME, SMd, and SME&d are all negative and zero with FSRE and FSRd < 1. For the situation illustrated in Figure 14b, the energy consumption during the rockburst exceeds the demand (ΔRECS > ED). One would expect a support system with more available capacity than required, to perform satisfactorily. The DBSD implicitly recognises the importance of the ground support during the rockburst process in the form of a bulking factor chosen to represent the support system’s efficiency in inhibiting the bulking. At the same time,
35 -
35 RECS
30 -
25 20 15 -
SME
10 500 a)
Extension of load path
Energy(kJ/m 2)
Energy(kJ/m 2)
30 -
ED
SMd
50 150 100 Bolt head and areal support displacement (mm)
00
RECS
SMd ED
50 150 100 Bolt head and areal support displacement (mm)
30 -
20 15 10 -
SME&d
SME dD ED
SMd
50 150 100 Bolt head and areal support displacement (mm)
Figure 13—Illustration of the safety margin definitions as proposed by Kaiser and Moss (2022) The Journal of the Southern African Institute of Mining and Metallurgy
Energy(kJ/m 2)
Energy(kJ/m 2)
SME dD
35 -
25 -
b)
Extension of load path
15 -
a)
30 -
00
20 -
5-
35 -
5-
25 -
10 -
SME&d
dD
ΔRECS Capacity consumed during rockburst unaccounted for
25 -
ΔRECS
20 15 10 500 b)
ED
50 150 100 Bolt head and areal support displacement (mm)
Figure 14—Unaccounted for energy consumption VOLUME 126
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A critical appraisal of the deformation-based support design approach the DBSD implicitly assumes that the support system experiences deformation, dD, and loses all the associated energy capacity ΔRECS (Figure 14) without performing any work. This assumption seems strange and is not stated or justified in the DBSD literature. As mentioned earlier, the demand path represents the increase in kinetic energy with displacement during a rockburst process, ignoring the effect of the ground support (except for assuming a BF appropriate to the effect of the ground support). The kinetic energy at the end of the bursting process is the ED that the support system is required to consume. In defining the FS, the DBSD approach does not seem to consider the deceleration action the ground support has on the moving mass. The moving rock mass is decelerated by the ground support until it comes to rest (kinetic energy = 0) or failure occurs. Let’s call this the energy consumption path, illustrated in Figure 15 by the purple line. The kinetic energy of the rock mass is the result of the demand path and consumption path and is conceptually illustrated in red. In light of Figures 14 and 15, it is difficult to see the significance of the crossing point between the load path and the RECS, and the meaning of the definition of FSs in the DBSD approach is not clear.
RECS as a state boundary Rigby et al. (2026) mentions the design capacity is reached when the demand path intersects the residual capacity curve (FS = 1), but redefines this point from ‘failure’ to ‘onset of damage’. With this redefinition, the calculated RECS curve is assigned a damage level of zero and a family of curves is generated to designate state boundaries for higher damage levels. For this, they employ the rockburst damage scale proposed by Kaiser et al. (1992) and Heal et al. (2006). A state of R0 damage is assigned to the calculated RECS curve. State boundaries for R3 and R5 damage levels are defined as RECS(d + ΔD) and RECS(d + 2ΔD). With these state boundaries, it is possible to assess the SM for different levels of damage. These state boundaries are illustrated in Figure 16 for the support system presented in Figure 7 of Kaiser and Moss (2022). This approach implies that after consuming all available support
capacity over 200 mm displacement, R0 conditions will prevail (point ‘1’ in Figure 16). It is difficult to envisage a support system after 200 mm displacement at the bolts, and an extra 200 mm displacement between bolts (assuming f = 1) to show no sign of damage. Figure 16 also shows a demand path and the ΔRECS are first presented in Figure 14. Interpreting the ΔRECs as a loss of support capacity during the rockburst (ignoring any work done by the support in the process), it is difficult to interpret the reduction of ΔRECS with increase in damage. It seems to imply that for a smaller rockburst with a demand path ending at point a (R0 damage), more support capacity is lost during the rockburst process (ΔRECR0) than for a larger event with R5 damage at point c (with ΔRECR5). Justification for the RECS to a state boundary appears to be that an exceedance of the design capacity at one location on the tunnel circumference does not imply failure or collapse of the entire excavation. This statement is, of course, correct and the interpretation consistent with the argument presented earlier for complex and non-uniform loading but is, however, inconsistent with the assessment of the RECS as discussed in an earlier Section of this paper. The assessment of the RECS assumes a mean support pressure resisting a uniform-piston like displacement. The formulation of the support system capacity in this way does not allow for distinction between the failure of a support element or multiple support elements and no distinction can be made between local and largescale failure. The reality of non-uniform reaction forces and variable displacement throughout the support system cannot be accounted for. It should be noted that the R0-R5 damage scale does not distinguish between damage extent and damage severity. Both the demand and capacity assessment components of the DBSD focus on the rockburst damage severity. The extent of damage is largely driven by the extent of rockburst demand. Accounting for damage extent with the simple addition of extra allowable displacement capacity lacks justification.
ΔRECR0 ΔRECR3 ΔRECR5
Energy capacity
(kJ/m²)
25 20 15
c 10
b a
5
1
0 0
50
100
150
ΔD ΔD
200
250
300
Central displacement R0 Figure 15—Conceptual illustration of load path and consumption paths and the kinetic energy of the rock mass resulting from their simultaneous interaction on the rock mass 568
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R3
R5
Figure 16—Residual support system capacity for R0, R3 and R5 rockburst damage levels The Journal of the Southern African Institute of Mining and Metallurgy
A critical appraisal of the deformation-based support design approach Table 2 Key assumptions and factors where supporting evidence or justification remains limited or uncertain No
Assumption/component
Influence
Notes
Component 1 - Estimation of system capacity 1
Constant load split over time.
Not clear
Does not seem reasonable.
2
Constant load split between different support elements.
Not clear
Does not seem reasonable.
3
Single load split value for different rockburst magnitudes and depths.
Not clear
Does not seem reasonable.
4
Load split values obtained from laboratory tests are representative for underground rockburst loading conditions.
Not clear
Currently no way to estimate the load split with confidence.
5
Relative displacement between support elements and surface support. f in Equation 3.
Unknown, depends on chosen value
f = 1 often implicitly assumed. No apparent reason to choose any particular value.
6
Uniform support loading mechanism according to ‘piston model’.
Optimistic
Seems contrary to observation.
7
Uniform strength capacity mobilisation.
Optimistic
Seems contrary to observation.
Components 3 and 4 - Demand estimation 8
Presupposition of bulking factor.
Unknown,depends on chosen value.
Leads to presupposition of outcomes.
9
Constant bulking depth over time.
Not clear
10
Constant bulking depth over space.
Not clear
Seems contrary to observation.
11
Constant bulking rate/constant rock mass acceleration.
Not clear
implicit assumption.
12
Linear bulking profile/linear velocity profile.
Not clear
13
Support system deformation during rockburst occur without any associated work performed.
Conservative
Implicit assumption inconsistent with 8.
Stability assessment 14
Failure is defined as the intersection between residual capacity and the demand path.
True only if 13 is true. Internally inconsistent with 8.
RECS interpreted as state boundaries 15
RECS = R0 damage.
Contradictory to assumptions made in assessing the RECS. Lacks justification.
16
R3 and R5 state boundaries can be defined by offsetting RECS with a specified displacement.
Conflates damage level and damage extent. Lacks justification.
Summary The previous sections have provided a detailed assessment of the DBSD approach and identified several underlying assumptions. Table 2 summarises the key assumptions and factors that may influence the resulting outcomes, particularly where supporting evidence or justification remains limited or uncertain. The extent to which these assumptions affect the results, and whether their influence is conservative or optimistic, is not always evident. The Journal of the Southern African Institute of Mining and Metallurgy
Further investigation, validation, and/or supporting evidence is required to provide confidence in the DBSD methodology. In addition to these factors listed, uncertainty about reasonable design values for several of the parameters also exist, leading to a wide uncertainty in the outcome.
Conclusion Analytical design approaches for rockburst-resistant ground VOLUME 126
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A critical appraisal of the deformation-based support design approach support remain attractive due to their promise of explicitly quantifying demand, capacity, and safety margin. Few analytical ground support design approaches for rockbursting conditions have been developed with the CRH approach being the best known with the most important further developments being the DBSD approach. The DBSD approach introduces several important conceptual improvements to the CRH approach. In particular, the emphasis on bulking velocity, residual support capacity, and displacement-based assessment of energy demand and capacity. Notwithstanding these advances, the appraisal presented in this paper demonstrates that the DBSD approach relies on a series of implicit and explicit assumptions that are difficult to justify and that remain largely unconstrained. The most important among these are the assumptions of uniform piston-like loading, simultaneous mobilisation of all support elements, constant loadsplitting behaviour, and constant representative bulking factors. Together, these assumptions oversimplify the complex, nonuniform interaction between a heterogeneous bursting rock mass and support system consisting of discrete elements at different locations. The cumulative effect is not clear and may result in either unconservative or overly conservative estimates of support capacity. The DBSD approach requires one to assume a constant (or characteristic) bulking factor with the calculated kinetic energy demand being directly proportional to the square of the chosen bulking factor. The DBSD approach assumes that the total bulking, bulking velocity, and energy demand are supressed by the installed ground support. When evaluating the safety margin, all work done during the bursting process is ignored. In this way, the DBSD approach makes internally inconsistent assumptions. This internal inconsistency undermines the physical interpretation of the defined factors of safety and the significance of the intersection between the demand path and the residual capacity curve is unclear. The reinterpretation of the residual capacity curve as a zerodamage state boundary, and its extension to higher damage states lacks justification. These extension of the RECS to state boundaries is also inconsistent with the assumptions and process for capacity estimation, which does not allow for distinction between local and large-scale failure. The role of the extent of the demand on the extent of damage is, however, ignored. The DBSD approach constitutes an important contribution to the ongoing discussion on dynamic ground support design and provides a useful framework for further discussion. However, in its current form, it does not overcome the problem of design indeterminacy associated with rockburst support design. Significant uncertainties in both model structure and parameter selection remain, and the quantification of safety margins continues to be subject to substantial ambiguity. The resolution of the identified issues is necessary to demonstrate that the DBSD methodology – when applied as a design tool – satisfies the standards expected of a robust engineering approach.
mine, Mongolia. The valuable contribution of colleagues at the ACG through discussion and feedback is gratefully acknowledged.
Acknowledgement
Kaiser, P.K., Malovichko, D.A. 2022. 'Energy and Displacement Demands Imposed on Rock Support by Strainburst Damage Mechanisms', Rockbursts and Seismicity in Mines, RaSiM10, 26–28 April 2022, Arisona, USA, Society for Mining, Metallurgy & Exploration, pp. 15.
This work was performed as part of the Mining Induced Seismicity and Rockburst Risk Management in Hard Rock Mines project at the Australian Centre for Geomechanics, The University of Western Australia. We acknowledge the contribution of the project sponsors: Agnico Eagle Mines, Canada; Alamos Gold, Canada; BHP, Australia; Glencore, Switzerland; Gold Fields, Australia; LKAB, Sweden; Newmont Corporation, Australia; Rio Tinto Oyu Tolgoi 570
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Kaiser, P.K., Moss, A. 2022. 'Deformation-based support design for highly stressed ground with a focus on rockburst damage mitigation', Journal of Rock Mechanics and Geotechnical The Journal of the Southern African Institute of Mining and Metallurgy
A critical appraisal of the deformation-based support design approach Engineering, vol. 14, no. 1, pp. 50–66, https://doi.org/10.1016/j.jrmge.2021.05.007 Kaiser, P.K., Tannant, D.D., McCreath, D.R., Jesenak, P. 1992. 'Rockburst damage assessment procedure', International symposium on rock support (Sudbury, 1992-06-16), 1992, A.A. Balkema, Rotterdam, pp. 639–647. Lilley, C.R., Roberts, T., Putzar, G., Beck, D.A. 2013. 'Dynamic simulations of excavations with yielding bolts', in Y Potvin & B Brady (eds), Ground Support 2013: Proceedings of the Seventh International Symposium on Ground Support in Mining and Underground Construction, 13–15 May 2013, Perth, Australian Centre for Geomechanics, pp. 525–538, 10.36487/ACG_rep/1304_36_Lilley Lowther, R., De Ross, J., Orrego, C., Cuello, D. 2022. 'A probabilistic evaluation of the displacement-based ground support design approach', in Y Potvin (ed.), Caving 2022: Proceedings of the Fifth International Conference on Block and Sublevel Caving, 30 August–1 September 2022, Adelaide, Australian Centre for Geomechanics, pp. 241–254, 10.36487/ACG_repo/2205_15 Malovichko, D. 2023. 'Utilisation of seismic data in the assessment of displacement and energy demand imposed on ground support by strainbursts', in J Wesseloo (ed.), Ground Support 2023: Proceedings of the 10th International Conference on Ground Support in Mining, 10–12 October 2023 2023, Perth, Australian Centre for Geomechanics, pp. 181–196, 10.36487/ACG_repo/2325_12 Malovichko, D.,Rigby, A. 2022. 'Description of seismic sources in underground mines: Dynamic stress fracturing around tunnels and strainbursting', arXiv, https://doi.org/10.48550/arxiv.2205.07379 Malovichko, D.A. 2022. 'Utility of Seismic Source Mechanisms in Mining', 10th International Symposium on Rockbursts and Seismicity in Mines, 26–28 April 2022, Tucson, Arizona, Society for Mining, Metallurgy & Exploration. Mikula, P., Gebremedhin, B. 2017. 'Empirical selection of ground support for dynamic conditions using charting of support performance at Hamlet mine', in J Wesseloo (ed.), Proceedings of the Eighth International Conference on Deep and High Stress Mining, 28–30 March 2017, Perth, Australian Centre for Geomechanics, pp. 625–636. https://doi.org/10.36487/ACG_rep/1704_42_Mikula
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Mikula, P.A. 2013. 'Progress with empirical performance charting for confident selection of ground support in seismic conditions', Transactions of the Institutions of Mining and Metallurgy: Section A, Mining Technology, vol. 121, no. 4, pp. 192–203, 10.1179/1743286312Y.0000000013 Mining Research Directorate. 1996. Canadian Rockburst Research Program 1990-1995, a comprehensive summary of five years of collaborative research on rockbursting in hardrock mines, CAMIRO Mining Division, Canada. Moss, A., Kaiser, P.K. 2022. 'An operational approach to ground control in deep mines', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 1, pp. 67–81, https://doi.org/10.1016/j.jrmge.2021.05.008 Ortlepp, W.D., Erasmus, P.N. 2002. 'The performance of yielding straps under dynamic loading', The Second International Seminar on Surface Support Liners, 2002, Sandton, The South African Institute of Mining and Metalurgy. Potvin, Y., Hadjigeorgiou, J. 2020. Ground support for underground mines, Australian Centre for Geomechanics, Perth. p. 520. Potvin, Y, Wesseloo, J & Heal, D 2010, 'An interpretation of ground support capacity submitted to dynamic loading', Mining Technology, vol. 119, no. 4, pp. 233–245, 10.1179/037178410X12886993781746 Primadiansyah, A.A., Eberhardt, E., Kaiser, P.K., Campbell, R., McMillan, R. 2025. 'Integrating Stress Fracturing and Bulking Monitoring for Deformation-Based Ground Support Design Calibration in a Deep Caving Operation', Rock Mechanics and Rock Engineering, 10.1007/s00603-025-04752-w Rigby, A., Malovichko, D., Kaiser, P.K. 2026. 'A Rockburst Hazard Assessment Framework for Strainbursting', Rock Mechanics and Rock Engineering, 10.1007/s00603-026-05461-8 Schultz, R.A. 1996. 'Relative scale and the strength and deformability of rock masses', Journal of Structural Geology, vol. 18, no. 9, pp. 1139–1149, https://doi.org/10.1016/0191-8141(96)00045-4 Stacey, T.R. 2016. 'Addressing the Consequences of Dynamic Rock Failure in Underground Excavations', Rock Mechanics and Rock Engineering, vol. 49, no. 10, pp. 4091–4101, 10.1007/s00603-016-0922-3 u
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Scraper winch safety and alternative cleaning technologies in South African narrow reef hard rock mines by M. Mpofu1, T.J. Otto², S. Ngobese1, N. Z. Nkosi³, F. Magweregwede³, N. Tlotleng³ Affiliation: 1 Council for Scientific and Industrial Research (CSIR) 2 University of Pretoria 3 Mine Health and Safety Council (MHSC) Correspondence to: M. Mpofu Email: mmpofu@csir.co.za
Dates:
Received: 04 Nov. 2025 Revised: .29 Mar. 2026 Accepted: 08 Sep. 2026 Published: Sep. 2026 How to cite: Mpofu M., Otto T.J., Ngobese S. Nkosi N.Z., Magweregwede F., Tlotleng N. 2026. Scraper winch safety and alternative cleaning technologies in South African narrow reef hard rock mines. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 9, pp. 573 – 584
Abstract
Scraper winches remain the primary machinery for moving broken rock in underground narrow reef hard rock mines in South Africa. Despite their longstanding use, significant safety hazards and risks associated with these systems remain. Recent analyses of scraperwinch-related accident data (2015 – 2024) confirmed that scraper winch ropes remain the main hazard and are often linked to insufficient situational awareness and task supervision. A review of scraper winch technologies and alternative broken rock cleaning systems revealed that most potential solutions were previously recommended through research initiatives like the Chamber of Mines Research Organisation, Deepmine, and Futuremine. The implementation of these potential solutions has been constrained by factors such as cost, reliability, and infrastructure compatibility. Key recommendations for improving safety in existing scraper winch operations include proactive rope condition monitoring, improved anchor point rigging, and the integration of proximity detection systems to reduce risks related to worker exposure to the moving parts of the scraper winch system. The implementation of alternative cleaning equipment such as waterjets for face cleaning and ultra-low profile dozers can further improve safety and production. However, the implementation of ultra-low profile dozers is limited by geological and infrastructure constraints. The above solutions focus directly on enhancing the scraper winch system or replacing it with alternative cleaning solutions. Alternatives that would result in the elimination of scraper winch hazards require a change in the mining method. Such solutions include mechanical, ropeless mining methods such as reef boring and reef cutting. Although these technologies offer safety advantages and a means to eliminate scraper-winchrelated risks, their economic viability in the South Africa’s narrow reef tabular environments remains challenging.
Keywords scraper winch, narrow reef hard rock mining, cleaning, safety improvements
DOI ID: https://doi.org/10.17159/24119717/3843/2026 ORCiD: M. Mpofu https://orcid.org/0000-0003-2010-7596 T.J. Otto https://orcid.org/0000-0002-3573-5627 S. Ngobese https://orcid.org/0000-0001-8125-9927 N.Z. Nkosi https://orcid.org/0009-0007-0285-8480 F. Magweregwede https://orcid.org/0000-0002-4824-2266 N. Tlotleng https://orcid.org/0000-0003-1526-2495
Introduction Scraper winches remain a fundamental aspect of ore handling in South African narrow reef hard rock mining operations. These systems are typically used to transport blasted ore from the stope face to the advance strike gully (ASG), then to the centre gully, and finally into the orepass (Rupprecht, 2013). Figure 1 represents a typical conventional stoping layout showing the face and the strike gully illustrating a scraper used for face cleaning. Figure 2 provides the layout of typical locations of scraper winches in a breast mining configuration. This figure shows two panels, their associated ASGs, and the centre gully pathways in which the broken material moves. Cleaning operations are critical in underground mining, particularly in narrow reef hard rock operations, where efficient and safe ore transportation directly affect production continuity and personnel safety (Rupprecht, 2011). Effective cleaning ensures that a continuous flow of material minimises operator exposure to hazardous conditions and enables smooth drill and blast sequencing. Additionally, it reduces the risk of equipment damage by keeping the scraper path clear of rocks; thereby, minimising rope abrasion, fouling, and shock loading on the winch. The continued use of scraper winches is largely due to their durability, suitability, and adaptability to confined working spaces and irregular ground conditions characteristic of narrow reef mining environments, relatively low capital expenditure compared to other mechanised equipment, and historical reliance on established
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Scraper winch safety and alternative cleaning technologies
Figure 1—Conventional stope layout depicting a face cleaning scraper system and strike gully (Rupprecht, 2013)
likelihood of incidents. The system’s complexity, involving moving parts such as ropes, snatch blocks, scrapers and winches (drums) within confined environments, further compounds operational risk. An analysis of South African Mining Industry (SAMI) winchrelated accident data for the period between 2015 and 2024 (Figure 3) revealed that being "struck by" an object is the leading cause, accounting for 43% (9 cases) of the incidents. This is followed by accidents where victims were "pulled by" winch components, contributing to 29% (6 cases) of the total. Fatalities categorised as "caught between" objects account for 10% (2 cases), while "slip and fall" and "punctured by" causes each represent 5% (1 case) of the total incidents. Between 1988 and 2002, Moseme et al. (2003) identified that scraper winches were responsible for approximately 5% and 9% of all accidents in underground gold and platinum mines, respectively. Accidents involving scraper winches have persisted at similar rates in the recent past in the narrow reef hard rock SAMI. Figure 4 shows the number of scraper winch-related fatalities recorded between 2015 and 2024, expressed as a percentage of total annual fatalities in the SAMI. Over this period, total fatalities in the SAMI declined significantly, from 90 in 2017 to a record low of 42 in 2024. However, this improvement is not reflected in scraper winch-related fatalities, which remain constant, at around 6.4%, peaking in 2022 with 10.2% of total annual fatalities. The relatively high operational risk can be partially attributed to the limited innovation in the scraper winch design, functioning, and control systems compared to recent safety solutions such as collision prevention systems (CPS) and innovations in other mining functions.
Historical overview and technological maturity Figure 2—A simplified layout of breast mining with scraper cleaning (Jacobs, 2021).
The earliest documented application of the transportation or handling of broken rock using scrapers was at the Bunker Hill and Sullivan mine in Kellogg, Idaho, in the United States of America (USA), where a slip scraper was used in the development of a tunnel (O’Brien, 1951). This is confirmed by Van Zyl (2022), who dates this first application back to 1898. In this region, a scraper winch, as it is known in South Africa, was referred to as a slusher, as illustrated in Figure 5. Developments in slusher and scraper technology peaked between 1920 and 1930, with the introduction of the two-drum hoist mechanism, the first electric-powered double-drum scraper and an increase in scraper capacities (Rhoades, 1982). In South Africa, mechanised rock handling using scraper winch technologies became prominent by 1932, and by 1946, their role in the SAMI was well established. The historic significance of the use and efficiency of
Figure 3—Immediate causes of scraper winch-related fatalities in the SAMI between 2015 and 2024
infrastructure (Coetzer, 2023; Kendall, Gericke, 2000; Rupprecht, 2019). This makes a shift to other technologies a significant undertaking. However, concerns remain regarding the ergonomic limitations and safety hazards posed by scraper winches. In conventional mines, scraper winches are operated manually using mechanical clutches, which demands considerable physical exertion and exposes operators to multiple hazards. Poor ergonomic positioning, obstructed lines of sight, and limited lighting in operational areas increase the risk of undetected hazards and the 574
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Figure 4—Scraper winch fatalities as a percentage of total SAMI fatalities, 2015 – 2024 The Journal of the Southern African Institute of Mining and Metallurgy
Scraper winch safety and alternative cleaning technologies
Figure 5—Illustration of historic scraper winch operations (O’Brien, 1951)
scraper installations in the SAMI is captured in the volumes “Gold Mining on the Witwatersrand” by C. Biccard Jeppe, first published in 1946 (Jeppe, 1946). The configuration of the scraper winch system and its functionality have remained fundamentally unchanged since its introduction (Van Zyl, 2022; Solomon, Van Niekerk, 1978). Furthermore, a review of historical literature reveals that many of the hazards associated with scraper winches today were already documented around 1950. O’Brien (1951), in the Canadian Mining and Metallurgical Bulleting, identified inadequate illumination as a persistent challenge for operators, who had difficulty seeing the scraper from a distance. This is similar to the working environment conditions in some South African narrow reef mines that continue to contribute to accidents today. Similarly, challenges associated with the anchoring of the tail-sheave (snatch block) to the hanging wall or footwall by means of eyebolts in drilled holes, was already identified by 1951 (O’Brien, 1951). In addition, breaking of the rope was identified as a frequent cause of delay and injury at that time, with ropes described as being subject to severe abrasion and sudden load application during the scraping cycle (O’Brien 1951). To address these and other challenges, safety improvements such as rope guards, enclosed drums, and operator shields were implemented in North American operations from at least the 1940s (Rhoades, 1982). In contrast, there have been significant improvements in the use of technology solutions in other mining functions. Recent advances in the SAMI show innovation in mining functions including ventilation where the use of digital twins for hazard modelling and ventilation-on-demand systems are being implemented (Jacobs et al., 2022), rock engineering where light detection and ranging (LiDAR) is used and thermal and acoustic imaging for rock hazard identification is gaining momentum (African Mining, 2023), and underground communications including the use of through- theearth (TTE) communication systems (Ngwenyama, WebberYoungman, 2024), while comparable innovations in scraper winch systems remain limited. Some of these advancements were borne out of research and development through initiatives such as COMRO, Deepmine, and Futuremine. These initiatives also resulted in recommendations for improving scraper winch safety, for example, innovation that included signalling systems, remote-controlled, automatic, and continuous scraper winches (Rupprecht, 2019). Despite the early foresight provided by Scott-Russell (1993), who stated that the The Journal of the Southern African Institute of Mining and Metallurgy
Figure 6—Various components of the scraper winch system including a winch, scraper, and winch rope (Bafotech, n.d.; Cape Gate, n.d; Galison Manufacturing, n.d.; Powerflow Scraper Winches, n.d.; Toco Lifting, n.d.; Wolfram Mining Supplies, 2024)
scraper winch was a “mature technology” facing diminishing returns, its use is still prevalent in the narrow reef hard rock mining sector. Minimal technology has been implemented to improve its functioning and innovation in the holistic scraper winch system has been limited (Coetzer, 2023; Pickering, 2007; Scott-Russell, 1993). This limited innovation has contributed to continued scraper winch related safety incidents.
Safety risks and contributing factors The scraper winch system comprises several components and key role-players: the scraper winch operator, additional personnel, the constrained and typically poorly lit working environment, the winch unit, the scraper or hoe, the blasted rock, the ropes, and associated rigging components. Figure 6 shows selected components of the scraper winch system, including three variations of winches (row A), three variations of scrapers or hoes (row B), and the winch rope, snatch block, D-shackle, and rigging chain (row C). Figure 7 highlights the challenge associated with the location of cleaning equipment, that is, in a confined gully. The interaction between these elements introduces various hazards that result in injuries and fatalities. A review of the major causes of scraper winch accidents by various authors (Ilgner et al., 2009; Moseme et al., 2003; Rupprecht, 2011; Sibanye-Stillwater, 2020) identified the following as recurrent contributing human-related factors: ➤ inaccurate hazard identification and risk perception; ➤ substandard operational practices and poor procedural adherence; ➤ inadequate, non-contextualised training for winch operators; ➤ weak supervisory enforcement of safe work protocols.
Figure 7—Double (left) and single (right) scraper configuration in cleaning in a strike gully (photos by author) VOLUME 126
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Scraper winch safety and alternative cleaning technologies Safety incidents involving scraper winch operations are typically the result of a combination of primary and secondary causes that originate from the hazards associated with the components of the scraper winch system and this interaction with underground personnel and the environment (Coetzer, 2023). These hazards include the winch drum, rope, anchor point rigging, scraper or hoe, and the snatch block. A particularly significant hazard is created within the “danger triangle”. This triangular zone is formed between the haul rope, return rope, and the snatch block anchor point, within which any personnel are at severe risk of being struck by a recoiling rope, a displaced rigging component, or a dislodged block of rock. Figure 8 illustrates danger triangle zones and the main components of a typical scraper winch system. The snatch block is a critical element in this system. The dynamic forces transmitted through the rigging setup, together with the confined and insufficiently lit underground conditions, make the proper installation and inspection of the snatch block rigging an ongoing challenge (Ilgner et al., 2009; Janicijevic, 2008). Additional causes of scraper-winch-related incidents include poor lighting and dust, which reduce visibility, lack of or inadequate preparation for cleaning by the day shift, blocked winch operator line-of-sight, and
Figure 8—The danger triangle associated with scraper winch operations (top image) and the hazard zone around the snatch block anchor point (bottom image)
the pressure to achieve key performance targets (Coetzer, 2023; Ilgner et al., 2009; Moseme, et al., 2003; Rupprecht, 2011).
Regulatory and guideline frameworks Despite the high incidence of scraper-winch-related accidents in the SAMI, comprehensive regulatory frameworks and best practice guidelines exist to govern their installation, operation and maintenance. Chapter 8.4 of the Mine Health and Safety Act (MHSA) No. 29 of 1996 establishes minimum safety and health requirements for the installation of scraper winches and mono-rope winches (South African Government, 1996). These regulations provide a foundation upon which mines develop their mandatory Codes of Practice (CoP), operational standards and training material to prevent safety incidents. Table 1 provides a summary of the key regulatory measures prescribed by the MHSA to prevent persons from being injured by scraper winches. Complementing these legislative requirements, Ilgner et al. (2009) developed a best practice guideline for scraper winch systems. The best practice guideline expands on regulatory provisions by detailing recommended approaches for design, installation, operation, and maintenance. In preparing the best practice guideline, Ilgner et al. (2009) found that most COPs were aligned with the regulations, and recommended that these COPs, mine standards, and work procedures be always adhered to. In addition, it was recommended that supervisors should monitor compliance frequently, with any non-compliance being addressed in a way that would prevent re-occurrence, instead of blaming the suspected offender. Ilgner et al. (2009) noted that compliance with standards can be achieved through: ➤ Routine visual inspections and checklists that are aligned with regulatory standards. ➤ Preventative maintenance schedules supported by up-todate maintenance logs. ➤ Thorough root cause analyses of incidents to implement sustainable corrective measures. ➤ Integration of monitoring technologies to identify potential hazards. ➤ Data-driven approaches to proactively mitigate risks.
Table 1
Summary of regulatory measures for scraper winch safety (South African Government, 1996) Category and chapter in MHSA
Regulatory focus
Key safety measures
Operator competency and authorisation Chapter 8.4 (2a and b)
Ensuring only trained and competent operators control scraper winches.
➤ Operators must be formally trained, authorised and declared competent.
➤ Equipment must be inspected and declared safe before use.
Warning and signalling systems Chapter 8.4 (2c and d)
Preventing accidental exposure of personnel to moving equipment.
➤ Audible or visual warnings must be provided before winch operation.
➤ Workers must have signalling systems to halt operations from any access point.
Inspection and monitoring Chapter 8.4 (2e)
Maintaining mechanical integrity and preventing equipment-related failures.
➤ Ropes, scrapers, attachments, and splicing must be ➤
Design and operational specifications Chapter 8.4 (2g and f)
Ensuring safe equipment design and proper use within rated capacities.
regularly inspected. Damaged components must be replaced immediately.
➤ Winch ropes must always be underlain. ➤ Ropes and attachments must be used within their design specifications and safe working limits.
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Scraper winch safety and alternative cleaning technologies The best practice guideline also recommends a shift in safety culture, encouraging supervisors to address non-compliance constructively by focusing on preventing recurrence rather than assigning blame. Although these frameworks are comprehensive, scraper-winch-related incidents persist across the sector, indicating a gap between regulation and implementation. This highlights the need for continuous evaluation of existing practices and the exploration of alternative cleaning technologies that inherently reduce operator exposure to hazards. The continued reliance on scraper winches highlights their importance in underground hard rock mining operations but the inherent challenges and risks associated with their use cannot be ignored. Recognising these challenges and in response to persistent scraper winch-related incidents and stagnant technology progress, the Mine Health and Safety Council (MHSC) commissioned a study (Centre of Excellence (CoE) 210501) to investigate the current use of scraper winches and their accessories, assess the safety performance, and to identify viable alternative cleaning solutions and technologies suited to South African underground operations. This paper presents the outcomes of that study, summarises the key findings, evaluates opportunities to improve safety and efficiency in cleaning practices, and outlines recommendations aimed at improving safety and modernisation in underground narrow reef hard rock transportation and cleaning processes.
Broken rock cleaning solutions and technologies This study investigated the current use of scraper winches and its accessories, assessed the safety performance, and identified viable alternative cleaning solutions and technologies suited to South African underground mining operations. The findings highlight persistent safety risks, implementation barriers, and opportunities for technological innovation. Analysis of accident data (2015 – 2024) revealed that scraper-winch-related incidents accounted for approximately 6% of total accidents in the SAMI, with 22% of fatalities in the transportation and mining category (Department of Mineral and Petroleum Resources, 2023). Most incidents occurred in the stope face (33%), followed by the raise or winze areas (24%) and the strike gully (24%). Key hazards included rope failures (35% of incidents), struck-by incidents (28%), and improper rigging (20%). Root causes included inadequate supervision, reactive maintenance practices, and insufficient training, consistent with findings by Moseme et al. (2003) and Ilgner et al. (2009).
Methodology The study used a mixed-methods research approach, incorporating both qualitative and quantitative techniques to investigate the use of scraper winches in underground mining operations, as well as evaluate alternative cleaning technologies. The mixed-methods approach was selected to combine the depth of qualitative insights into operational practices and stakeholder perspectives with the objectivity of quantitative analysis of accident data, ensuring a holistic understanding of the research problem. Qualitative data was gathered through three primary methods. Firstly, semi-structured interviews were conducted with stakeholders from 10 organisations, including mining companies, original equipment manufacturers (OEM), and technology suppliers. Secondly, observations of cleaning-related activities were conducted at one narrow reef gold and one platinum mine during both day and night shifts. Thirdly, a workshop was held with various mining industry stakeholders (including OEMs, mining operation The Journal of the Southern African Institute of Mining and Metallurgy
workers, safety and labour representatives, and technology suppliers) to gain their insights on the use of scraper winches and alternative cleaning equipment. The workshop, attended by more than 40 participants, discussed challenges with scraper winches and evaluated the feasibility of alternative cleaning technologies. Qualitative data from interviews and observations emphasised systemic barriers to safety compliance. Despite established standards under the Mine Health and Safety Act (MHSA) of 1996, Chapter 8.4, adherence was inconsistent due to spare part shortages, production pressures, and rushed safety assessments. Operators reported visibility challenges in advance strike gullies due to obstructions and poor lighting, exacerbating risks of undetected hazards. It is notable that these challenges are still prevalent in the mining industry more than 20 years after being identified by Moseme et al. (2003). Training programmes were often generic, lacking context-specific problem-solving, leading to procedural deviations. Similarly to the observations of Rupprecht (2013) on maintenance lapses, manufacturers noted unauthorised equipment modifications, particularly to winch starter boxes and winch ropes, as a significant safety concern. Quantitative data were obtained by analysing historical accident data related to scraper winches from 2015 to 2023 (Department of Mineral and Petroleum Resources, 2023) as well as industry reports. The dataset included more than 1 200 accidents, with variables such as accident type, cause, demographic information of employees (e.g., age, working experience), and location of accidents. This analysis aimed to identify trends, frequencies, and contributing factors to scraper-winch-related accidents. Quantitative data was analysed through descriptive statistical methods to identify trends, frequency distributions, and correlations between the types of accidents, their causes, and profile of the workers that were involved in these accidents. Graphical plots were used to visualise patterns and supplement the data interpretations. The results of the quantitative analysis were then integrated with the qualitative data analysis results to provide an aligned understanding of the underlying causes of scraper-winch-related accidents and potential solutions to addressing the challenges. In addition to the qualitative and quantitative analyses, an option analysis approach was adopted to assess the potential solutions identified through literature and stakeholder inputs. Each of the solutions, categorised into enhancements to existing scraper winch operations, alternative cleaning technologies, and alternative mining technologies and layouts, were evaluated against criteria such as safety impact, implementation feasibility, operational efficiency, and cost effectiveness. Ethical approval was obtained from the CSIR Research Ethics Committee. Informed consent was secured from all interview and workshop participants, ensuring anonymity and confidentiality.
Criteria for evaluating potential solutions and technologies Potential solutions and technologies were evaluated against four criteria namely safety impact, implementation feasibility, operational efficiency, and cost-effectiveness. The choice of the criteria is briefly explained as follows: ➤ Safety – scraper winches are associated with high accident rates, including fatalities and injuries caused by entanglement, rope failures, and operator errors. Evaluating solutions based on their ability to mitigate these risks is crucial for compliance with safety regulations and achieving SAMI "zero harm" goals. VOLUME 126
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Scraper winch safety and alternative cleaning technologies ➤ Implementation feasibility – this refers to the practicality of implementing a solution within existing mining layouts, infrastructure, and worker skills and capabilities. Implementation feasibility is particularly important in old gold and platinum group metals (PGM) mines with limited shaft space for transporting bulky, non-modular equipment. ➤ Operational efficiency – underground mining operations rely on efficient rock handling systems to maintain productivity. Evaluating solutions for their impact on operational efficiency ensures that productivity is not compromised while implementing safety measures. ➤ Cost-effectiveness – mines, particularly those in the gold and PGM sectors where scraper winches are used, operate under tight financial constraints. Therefore, it is vital to evaluate solutions based on cost-effectiveness to ensure they are financially viable and of value. Solutions to addressing these challenges are discussed in the context of three categories namely enhancements to existing scraper winch systems, adoption of alternative cleaning technologies, and transition to ropeless cleaning methods.
Figure 9—Standard PDS installation layout in a gully (adapted from Giacomo, 2020)
Enhancements to existing scraper winch operations Improving existing scraper winch systems requires a comprehensive and balanced approach that enhances worker safety, operational efficiency, and adherence to standards. Enhancements to existing scraper winch systems were evaluated as short-term solutions to improve safety while leveraging current infrastructure. Van Zyl (2022) indicated that there is no one perfect solution to address winch-related accidents. Three notable interventions namely the installation of PDS on scraper winches, the introduction of scraper winch rope condition monitoring (and rope discard criteria), and improvements to the rigging of snatch blocks show good potential. Firstly, a PDS-installed scraper winch system could potentially provide significant safety improvements, reducing the risk of accidents related to workers entering the scraper path by generating alerts and warnings, and shutting down the winch. Thus, PDS can mitigate "struck by" and “pulled by” incidents involving both the scoop and the rope, as the system detects personnel proximity to the entire hazard zone rather than to a specific component. Figure 9 illustrates the typical configuration of a modular PDS system including the placement of proximity signalling devices along the scraping path. The technology addresses the need for reliance on the winch operator or miner to stop the winch in case of an emergency. Although initial capital costs and integration challenges exist, this solution promises to have long-term benefits including reducing injuries and fatalities and the associated operational disruptions. Figure 10 shows the installation of such a PDS system in a raise line as part of a project at an underground PGM mine in South Africa where a proximity detection-enabled signalling system was installed on the centre-line winch and gulley winch. The scope of the project was to implement a winch PDS system that retains the normal functionality of the existing signalling system, and is able to detect the presence of persons within the scraper path (Sibanye-Stillwater, 2024). From Figure 11 it can be seen that a comprehensive decisionmaking process with various nodes is a key requirement for the successful implementation of a winch PDS system where the PDS functionality is intended to serve as a last-line-of-defence fail-safe function and not to replace manual signalling (Strydom, Conolly, 2020). 578
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Figure 10—Winch PDS installation in raise line (Sibanye-Stillwater, 2024)
Figure 11—Winch PDS decision-making flow (Strydom, Conolly, 2020)
Key considerations for the introduction of a scraper winch proximity detection system (PDS) include clarity on exclusion zones, reaction times of the system, effects of system stoppages and start-ups, effectiveness of warning signals and alerts, visualisations to improve operator’s line of sight, and data logging and analytics. Additionally, deployment of a PDS for scraper winches should consider the conditions of the operational environment, which include the presence of dust, water, and rock falls. The PDS components should therefore be ruggedised and positioned optimally, away from possible damage. Secondly, implementing scraper winch rope condition monitoring through time-bound inspections or using sensor technology reduces the risk of accidents occurring due to rope The Journal of the Southern African Institute of Mining and Metallurgy
Scraper winch safety and alternative cleaning technologies breakages. Rope breakage causes fatalities or injury through the whiplash effect, resulting from the sudden release of stored energy. The broken rope is propelled towards the winch or anchor points at high velocity, striking any personnel in its path. While PDS is designed to detect personnel entering a hazard zone, the early detection of rope wear, damage, deformation, or tension anomalies can improve both safety and operational reliability through the prevention of fatalities and reduced downtime. Scraper winchrope-conditioning monitoring would require the development and enforcement of discard criteria, which will address the current risk associated with using ropes until failure. Importantly, the criteria should specify the regularity of scraper winch inspections. In parallel with condition monitoring, research into improved rope materials and designs provides an opportunity for additional safety and operational benefits. Research conducted by Felber et al. (2024), shows that high-modulus-high-tenacity (HM-HT) fibre ropes are increasingly used in mining due to their corrosion resistance, high breaking force, good bending flexibility, and lower density compared to steel wire ropes. However, these technologies and other hybrid (steel-fibre) ropes require further testing and validation in the harsh narrow-reef environment, particularly regarding abrasion resistance and long-term durability under dynamic loading (Wang et al., 2024). Thirdly, safely rigging the snatch block or return pulley of a scraper winch is a key safety control to mitigate the uncontrolled release of mechanical energy and prevent localised hangingwall failures. During cleaning operations, the scraper rigging exerts substantial horizontal forces. These forces directly transfer to the rock anchor and could dislodge unstable blocks of rock along planes of structural weakness. The geo-mechanical analysis, provided in Appendix 1, indicates that if an anchored rock is dislodged, the combination of gravity and constant scraper tension accelerates the rock. In a standard 1.4 m stoping width, a dislodged rock could be moved laterally by more than a metre before making contact with the footwall. Improved rigging design should consider the optimal tendon installation angle. Installing tendons vertically or perpendicular to a horizontal plane of weakness is not effective, as the tendons provide limited geometric resistance to the lateral pulling force of the scraper rope. Based on the geo-mechanical analysis provided in Appendix 1, the optimal installation angle is equal to the friction angle of the rock interface. Therefore, designing and implementing an angled tendon layout based on the MohrCoulomb stability criteria, accounting for rock cohesion, friction, and expected scraper-induced shear stresses, should ensure the integrity of the snatch block rigging point while minimising the localised hangingwall failures and the required number of bolts. In addition to these solutions, other supporting measures such as remote-control winch operations, improved training quality, time-task allocation, and hazard identification and risk assessment further strengthen operational efficiency. These solutions are low cost and focus on human factors and operational discipline, which complement the technological solutions, thus, providing a holistic approach to enhancing existing scraper winch operations. However, the practical implementation of these solutions must consider an important factor, the limited face time available per shift. Site investigations (Mpofu et al., 2025) revealed that extended travel times from surface to underground working areas substantially reduce the effective time available for cleaning, compressing task schedules, and creating pressure to complete critical activities within the shift. This results in rushed risk assessments and machine The Journal of the Southern African Institute of Mining and Metallurgy
inspections in order to allocate sufficient time for actual cleaning. This time-task constraint also affects shift supervisors, who are responsible for conducting planned task observations (PTO) in multiple areas. This could lead to the PTOs turning into a “paper exercise” defeating their intended purpose of proactive hazard identification and quality control.
Adoption of alternative cleaning equipment Alternative cleaning technologies, such as waterjets and ULP dozers, significantly reduce direct worker exposure to dangerous scraper winch components, thereby lowering the risk of injuries caused by rope failures, snatch blocks, and moving scrapers. Replacing scraper winches with alternative cleaning equipment requires site-specific evaluation of feasibility, layout constraints, and operational readiness. Based on the options analysis, these solutions show significant potential to enhance safety by eliminating hazards associated with scraper winch ropes and operator exposure. The use of high-pressure water jets, particularly for moving broken rock from the face, significantly reduces the need for face scraper winches (ASG and centre-gully winches are typically still required) and ropes; thus, eliminating related safety risks such as rope entanglement and scraper pathway incidents. However, water jets introduce new challenges such as flooding, high-pressure water hazards, and mud rushes, specifically in Upper Group (UG2) reefs. Additionally, water jets perform optimally on dipping reef and short faces of approximately 15 m (O’Beirne, 1979), thereby limiting their implementation feasibility. When considering water jets, the following factors must be assessed: Mud rushes – Mud rushes are sudden inflows of water-saturated broken rock and fines from drawpoints or other underground openings (Butcher et al., 2005). Since water is one of the triggers for a mud rush, the use of waterjets increases the risk of mud rushes occurring. ➤ Water management – waterjets introduce additional water into the system. New operations should be designed to handle the water. In existing operations, the infrastructure may not be able to handle the increased water volumes, leading to flooding of some working areas. The FutureMine research programme (Peake et al., 2002) documented that many mines lack effective stope water management, with water demand exceeding 2.4 tonnes per tonne mined in some cases, which is above the industry rule-of-thumb of 1 tonne/t. This suggests that existing infrastructure may have difficulties in handling the additional water introduced by waterjets. ➤ Pumping costs – the additional water results in an increase in pumping costs and, as a result, energy consumption costs. ➤ Dip constraints – the mine layout and gully design inclination should not hold water. Flat dipping orebodies may not be compatible with the use of waterjets. ULP dozers are a notable technological development for mechanised cleaning in narrow reef operations, with several OEMs having developed and trialled prototype and production units in South African and platinum mines (Fourie et al., 2017; Harrison, 2006; Olsen et al., 2008). However, their industry-wide adoption remains limited by various operational challenges. These dozers are designed to clean mined material from underground places of work and remove it into the ASG (Fourie et al., 2017). Figure 12 illustrates a ULP dozer cleaning the panel. By combining remote operation, VOLUME 126
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Scraper winch safety and alternative cleaning technologies PDS, and CPS, ULP dozers remove the workers from hazardous zones and thereby improve safety. Although this solution has longterm benefits, including higher cleaning rates and reduced exposure of workers, it requires high initial investment and significant changes to current access and transport excavations as well as mining layouts. The use of ULP dozers could have a positive safety impact due to their integration with PDS and CPS. Introducing this technology requires considerable changes to mining layouts, specialised skills, and a relatively high initial capital investment. Similar to waterjets, the feasibility of adopting ULP dozers for stope cleaning in current operations needs to be validated and should follow a holistic approach since their use is affected by: ➤ Infrastructure limitations – current infrastructure in conventional mines is not suited for the transport of ULP dozers, from the shaft to the stope face. ➤ Dip constraints – their functionality is limited by the dip of the orebody. ➤ Maintenance – routine maintenance activities should be scheduled, and adequate maintenance capacity and capability is required. ➤ Breakdown management – when ULP dozers break down in the stope environment they would need to be fixed in their position, meaning that other activities would be affected. ➤ Battery performance – the recharging duration of batteries may affect productivity. ➤ Remote-control capability – the ability to use remote control should not eliminate the need for the operator to have a clear line-of-sight while operating a dozer.
Transition to mining methods that eliminate the need for scraper winches Several mining methods have successfully been used to mine and clean narrow reef orebodies without relying on scraper winches. The transition to ropeless mining methods, that eliminate the need for scraper winches, has the potential to provide the highest safety impact by removing workers from high-risk zones. Ropeless solutions face barriers such as capital costs, infrastructure modifications, and workforce upskilling requirements, particularly in established mines with legacy infrastructure. Transitioning to ropeless mining methods and layouts requires careful planning and substantial investment. The application of these alternative mining methods and technologies is site-specific and more suited for new mines or extensions of current operations. These operations would need to follow a holistic decision-making process and evaluate alternative designs that could eliminate the scraper winch and legacy infrastructure constraints.
A number of mining methods can result in the elimination of scraper winches. Reuse mining and selective blast mining techniques, primarily used to reduce ore dilution can be used with water jets or dozers, eliminating the need for scraper winches and the associated hazards. However, these methods require modifications to existing layouts and careful management of drilling and blasting accuracy (Bock, 1996). A related technique, known as throw blasting, where explosive energy is used to propel broken rock into an advanced strike drive (ASD), has been considered in mechanised narrow reef layouts. Pickering (2006), reported on trials conducted for longhole drilling and the use of throw blasting in combination with waterjets for cleaning at the Union Section of Anglo Platinum mines. Similarly, Fourie et al. (2017), detail a scattered breast mining layout that uses throw blasting to propel fragmented rock to ASDs, with the remainder thereof being cleaned using load-haul-dump (LHD) trucks. However, the widespread adoption of throw blasting as a means to improving cleaning has not been achieved. Mechanised breast mining, which incorporates machines such as conveyors or LHDs, and the associated integration of PDS and CPS completely removes the need for scraper winches, improving worker safety and operational efficiency. Implementation of this method is, however, limited by the associated high capital investment and the overhaul of existing mining layouts and infrastructure. In the PGM sector, mining operations are increasingly shifting from conventional narrow-reef stoping towards room-and-pillar (bord-and-pillar) mining (McGill, 2011; Watson et al., 2024). However, the introduction of this mining method is associated with relatively higher capital expenditure and wider mining widths. Furthermore, the method introduces new operational risks such as collision hazards related to trackless mobile machines and increased ventilation demands. Similarly, mechanised mining methods and technologies for breaking rock, such as reef boring and reef cutting remove the workers from hazardous areas and improve productivity and ore recovery. These methods, however, require substantial upfront investment and the redesign of mining layouts. Fundamentally, their introduction requires a substantial understanding of the site geology and geotechnical factors as these affect excavation parameters such as gradient of the excavation, fragmentation of the rock, and the types of support required (Van der Bijl, 2024; Roos, 2025). Reef boring and reef cutting can therefore be regarded as long term solutions in comparison to solution such as the incorporation of PDS to scraper winches or the adoption of winch rope condition monitoring. Ultimately, eliminating scraper winches in underground mining requires a systemic, long-term approach. While mechanised alternatives promise safety and productivity benefits, their adoption must be aligned with site-specific geological and operational conditions to achieve sustainable results. Since mechanised alternatives have proven difficult to implement in deep-level narrow reef hard rock mining, despite decades of research and trials, critical evidence and lessons learnt should inform the evaluation of any future alternatives.
Conclusions
Figure 12—ULP dozer cleaning a panel (Fourie et al., 2017) 580
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Scraper-winch-related challenges and accidents have remained consistent over many years in underground narrow reef hard rock mines in South Africa. This highlights slow progress in introducing innovation to address known safety and operational risks. Persistent hazards, including rope degradation and failure, inadequate rigging, and unsafe practices, continue to contribute to scraper-winchThe Journal of the Southern African Institute of Mining and Metallurgy
Scraper winch safety and alternative cleaning technologies related accidents. Although several safety improvement solutions have been proposed in the past, their adoption has been limited by factors such as high capital costs, perceived reliability issues, and infrastructure constraints. Opportunities remain to improve safety and efficiency through the integration of practices such as proactive rope condition monitoring and improved rigging designs as well as technologies such as PDS. Introducing a proactive approach to scraper winch rope monitoring and replacement through an inspection routine and improved snatch block rigging designs and installations are examples of the adoption of a best practice approach to reducing accidents caused by scraper winches. The integration of PDS with scraper winches, although a mature technology in other sectors, is still in its early stages within the mining industry and has yet to gain significant traction as a viable solution to reduce scraper-winchrelated incidents. Several alternative cleaning equipment options that exist can be used either in conjunction with scraper winches or as complete replacements. Waterjets are considered one of the best alternatives for face cleaning, primarily due to their enhanced ropeless safety features. ULP dozers are also considered safer solutions for cleaning, however, they require major mine layout modifications, relative to conventional breast mining layouts, and substantial upfront investment. Further research and operational validation are required before ULP dozers can be considered a proven, viable technology for industry-wide adoption in South African narrow-reef mining. Considerations should be made on transitioning to mining methods that eliminate the use of scraper winches completely. Mechanical rock breaking methods such as reef boring and reef cutting remove the worker from hazardous working environments; however, their adoption requires further research, skills forecasting, and investment. The dynamic nature of scraper winch operations necessitates a systemic, data-driven approach to safety improvements. By improving existing systems and integrating advanced technologies, the mining industry can achieve significant progress toward its "zero harm" goal while maintaining operational efficiency and sustainability.
Recommendations for future research The following are recommendations for future research that can make an impact to the mining industry: ➤ Further research into the application of PDS technology for scraper winch systems, remote controlled winches, and enhanced visualisation and communication systems in underground mining environments. ➤ Investigation of scraper winch rope-and-rigging-related safety aspects including defining a scraper winch rope inspection and replacement protocol, and the evaluation of the long-term durability and cost-effectiveness of alternative rope types, such as synthetic fibre ropes, for narrow reef scraper winch operating conditions. ➤ Further work to investigate improved methods to attach gully scraper rigging is recommended. ➤ Supplementary research is recommended to quantify the capacity of existing mine water reticulation systems to handle additional water volumes from waterjet cleaning. ➤ Further research into the applicability, available technology solutions, key enablers, and benefits of mechanical methods such as reef boring and cutting The Journal of the Southern African Institute of Mining and Metallurgy
as a safe and productive alternative to scraper winch operations.
Appendix 1 Introduction Figure A1 illustrates a conceptual snatch block anchoring problem assuming a block of rock with mass m is anchored by N number of tendons installed at any arbitrary angle β to the horizontal. The block is separated from the hangingwall by a horizontal plane of weakness with a cohesion C and a friction angle ϕ. The contact area of the block is A. The scraper rigging is attached to the block and it exerts a horizontal force on the block. This problem posed, is considered as the worst-case scenario as there are no other kinematic constraints, e.g., no adjacent blocks to clamp it in place.
Problem statement
➤ Derive a general solution for the number of bolts with a capacity of Fbolt required to keep the block stable. ➤ If the block gets dislodged, how much lateral displacement will occur before the block makes contact with the footwall.
Number of bolts required Assume the shear stress τ on the interface of the block is given by the Mohr-Coulomb strength criterion: [1] The contact area is A and therefore: [2]
or
[3] The normal force (positive value for compression) acting on the interface (if the bolts are prestressed and NFbolt sinβ>mg) is: [4] When inserting Equation 4 in Equation 3, it gives: [5] For the block of rock to maintain stability, the forces need to be in equilibrium and therefore:
Figure A1—Snatch block anchoring configuration VOLUME 126
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or
width): [14]
[7] This can be rewritten as:
is:
By rearranging Equation 14, the time it takes for the block to fall
[8] The number of bolts required is therefore: [9] For the case where it is a loose block (C = 0 MPa, ϕ = 0°) being suspended by the tendons, Equation 9 simplifies to: [10] This gives the expected result, e.g., if β = 0°, then NFbolt= Fscraper. Of significance is that if β= 90°, N is not defined (infinitely high). This implies that if the tendons are installed vertically and their bending strength is ignored (e.g., hydrabolts are weak when subjected to bending forces), they serve no purpose to resist the pulling force of the scraper. For securing the hangingwall in the area where the scraper rigging is attached, it is therefore counterproductive to install the tendons vertically.
Consider the following example Fscraper = 2 tonnes = 20 kN (force exerted by the scraper on the block of rock) m = 2.7 tonnes (mass of the block of rock) β = 60° (installation angle of the tendons) ϕ = 30° (friction angle of the rock interface) C = 5 kPa (cohesion of the rock interface) A = 2 m x 1.5 m = 3 m2 (contact area of the block of rock with the hangingwall) Fbolt = 1 tonne = 10 kN (force exerted by the tendon) When inserting these values in Equation 9, it follows that:
This implies that at least three tendons need to be installed to keep the block stable (for the assumed parameters).
Lateral displacement of a dislodged block Consider the example where the combined force of gravity and the scraper dislodge the block. The acceleration of the block is given by F = ma or a = F/m. The velocity v of the block is: [11]
[15] For a stoping width of 1.4 m, it therefore takes t = 0.53 s for the block to fall from the hangingwall to the footwall. By inserting Equation 15 in Equation 13, it follows that the lateral displacement is: x=(F_scraper h)/mg
[16]
By using the parameter values of the example in the aforesaid, it follows that the lateral displacement of the block will be x = 1.06 m by the time it comes into contact with the footwall.
Pullout forces (critical bond lengths) of splitsets
Figure A2 illustrates the pullout forces of splitsets. These in situ tests were conducted at a South African platinum mine. If only 300 mm of a splitset protrude through the block, it is estimated that the pullout force will be less than 2 tonnes. For the example above, 1 tonne was assumed.
Key insights
Regarding the stability of scraper rigging attachments, the analysis has indicated that installation of tendons normal to the dip of the reef in these areas (to reinforce the rigging point) is not necessarily the optimum angle. The analysis also indicated that, even with moderate forces being applied by the scraper rope, a large dislodged rock can be displaced laterally for a considerable distance before it makes contact with the footwall.
CRediT Author statement
MM: Funding acquisition; Methodology; Investigation; Formal analysis; Writing – original draft preparation; Project administration TJO: Formal analysis; Writing - original draft preparation; Supervision; Validation SN: Methodology; Investigation; Formal analysis; Writing – original draft preparation NZN: Supervision; Writing - review and editing; Project administration FM: Supervision; Writing - review and editing; Project administration
The displacement, d, of the block is [12] From Equation 12, it follows that the lateral displacement of the block is [13] The time, t, is the time it takes for the block to fall from the hangingwall to the footwall (it is assumed the scraper force stays constant during the fall). This time can be calculated from Equation 12 using the gravitational forces on the block (h is the stoping 582
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Figure A2—Pullout forces of splitsets The Journal of the Southern African Institute of Mining and Metallurgy
Scraper winch safety and alternative cleaning technologies NT: Supervision; Writing - review and editing; Project administration
Acknowledgements
We would like to thank the Mine Health and Safety Council (MHSC) for funding this project. We also thank the MHSC Programme Delivery Technical Committee (PDTC) and the Safety in Mines Research Advisory Committee (SIMRAC), and the Mine Occupational Safety and Health (MOSH) Transport and Machinery team of the Minerals Council South Africa for their guidance and technical insights. Finally, we are grateful to the mining industry, stakeholders and individuals who participated in this project at all levels.
Referencesw
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NATIONAL & INTERNATIONAL ACTIVITIES 2026 7-11 September 2026 — Electra Mining Africa 2026 Expo Centre, Nasrec, Johannesburg Contact: Gugu Charlie Contact: Camielah Jardine Tel: 011 538-0237 Website: http://www.saimm.co.za 21-24 September 2026 — XIX 2026 International Society for Mine Surveying Congress Century City, Cape Town Contact: Gugu Charlie Tel: 011 538-0238 Website: http://www.saimm.co.za 29-30 September 2026 — 7TH Young Professionals Conference 2026 Mintek, Randburg, Johannesburg Contact: Gugu Charlie Tel: 011 538-0238 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 1-2 October 2026 — Mintek SCi- (Science Convention for Innovators in Science, Engineering and Technology) Mintek, Randburg, Johannesburg https://seismix2026.co.za/ 5-9 October 2026 — The 21st International Symposium Deep Seismic Profiling of the Continents and their Margin Skukuza, Kruger National Park Website: http://www.saimm.co.za 12-16 October 2026 — 19TH South African Geophysical Association Conference 2026 Lagoon Beach Hotel & Spa, Cape Town Website: https://sagaconference.co.za/ 18-22 October 2026 — IMPC 2026 XXXII International Mineral Processing Congress Empowering a future-fit mineral processing industry Cape Town, South Africa Contact: Camielah Jardine Tel: 011 538-0237 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za
The Journal of the Southern African Institute of Mining and Metallurgy
11-13 November 2026 — MineSafe 2026 Mine Health and Safety Conference Engineering Health & Safety For Sustainable Future-Ready Mines Gallagher Convention Centre, Johannesburg, South Africa Contact: Gugu Charlie Tel: 011 538-0238 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 15-18 November 2026 — Southern African Mine Water Conference 2026 Let’s Connect Glenburn Lodge, Muldersdrift Contact: Gugu Charlie Tel: 011 538-0238 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za GMG’s 2026 Mid-Year Report For more information https://gmggroup.org/wp-content/uploads/2026/07/GMGMid-Year-Report-2026.pdf 28 February -3 March 2027 — MINEXCHANGE 2027 Denver, Colorado Colorado Convention Center MINEXCHANGE 2027 SME Annual Conference and Expo 5-9 April 2027 — Hoist and Haul 2027 Century City, Cape Town Contact: Gugu Charlie Tel: 011 538-0238 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 23-27 May 2027 — The International Ferro-Alloys Congress (INFACON) XVIII Reykjavik, Iceland Website: https://www.infacon18.com/
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Company affiliates The following organizations have been admitted to the Institute as Company Affiliates CorporateName
Weir Minerals Africa
Agreenco Environmental Projects
Herrenknecht AG
OPTRON
Becker Mining (Pty) Ltd
Wekaba Engineering (Pty) Ltd
Rosond (Pty) Ltd
Jones & Wagener Pty Ltd
Sound Mining Solution (Pty) Ltd
CYMOT (PTY) Ltd
Roytec Global (Pty) Ltd
Kelnir Projects (Pty) Ltd
Caledonia Mining South Africa
DQS Pty Ltd
Elbroc Mining Products (Pty) Ltd
M84 Geotech Pty Ltd
Modular Mining Systems Africa (Pty) Ltd
Sulzer Pumps (South Africa) (Pty) Ltd
Paterson & Cooke Consulting Engineers (Pty) Ltd
EKATO South Africa
SENET (Pty) Ltd
CARBOCRAFT (PTY) LTD
Tomra (Pty) Ltd
Solenis
Ukwazi Mining Solutions (Pty) Ltd
Pump and Abrasion Technologies (PTY) Ltd
Arcus Gibb (Pty) Ltd
CIGroup ACE Pty Ltd
SRK Consulting SA (Pty) Ltd
Sebotka (Pty) Ltd
ZUTARI (Pty) Ltd
Mech-Industries (Pty) Ltd
Minerals Council of South Africa
Anton Paar Southern Africa
Bluhm Burton Engineering Pty Ltd
Trans-Caledon Tunnel Authority
Hatch (Pty) Ltd
acQuire Technology Solutions
Anglogold Ashanti Ltd
EHL Consulting Engineers (Pty) Ltd
Digby Wells and Associates
DDP Specialty Products South Africa (Pty) Ltd
AMIRA International Africa (Pty) Ltd
Allied Furnace Consultants
Malvern Panalytical (Pty) Ltd
Gravitas Minerals (Pty) Ltd
MineRP Holding (Pty) Ltd
De-Tect Unit Inspection (Pty) Ltd
FLSmidth Minerals (Pty) Ltd
Mining Projection Concepts (Pty) Ltd
Glencore
African Pegmatite
Murray & Roberts Cementation (Pty) Ltd
Ex Mente Technologies (Pty) Ltd
Redpath Mining (South Africa) (Pty) Ltd
MLB Investments CC
Sibanye Gold Limited
Micromine Africa (Pty) Ltd
AECI Mining Chemicals, a division of AECI Mining Ltd
Ingwenya Mineral Processing
Exxaro Resources Limited
Maptek (Pty) Ltd
IMS Engineering (Pty) Ltd
G H H Mining Machines (Pty) Ltd
Rustenburg Platinum Mines Limited - Union
OPTRON
Impala Platinum Holdings Limited
Geobrugg Southern Africa (Pty) Ltd
Mintek
Ivanhoe Mines SA
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Drilling and Blasting Short Course 2027
This short course is sponsored by AECI Mining Explosives
Drilling and Blasting SHORT COURSE Short 2024 DATE:Course 24-25 FEBRUARY 2027 VENUE: ONLINE
ECSA and SACNASP Validated CPD Activity Credits = 0.1 per hour attended
BACKGROUND There has been a significant change in the explosives and initiating systems used in the mining, quarrying and civil blasting applications both on surface and underground. This course will provide and align the delegates with some basic principles, tools, examples and understanding of the leverage of these products. Whether you are new to the industry or a seasoned user or find yourself in the position of an explosive manager or supervisor to a regulator, the course will enable some debate, rules and questions you should be asking of your explosive OEM. The importance of improved safety standards, cost effectiveness and productivity has driven mining management and operators to examine all facets of their operations. Increasingly it has been realized that an efficient drilling and blasting program can impact positively throughout the mining operation from loading to maintenance, hauling to crushing, ground support to scaling and grade control to recovery. We will also test the concepts and increasing challenges of blasting in the vicinity of local communities. .
PRESENTER
Simon Tose Simon Tose is an expert in Blast Design from AECI Mining Explosives and will be sharing his expertise on this subject.
Simon Tose is an established industry recognized consultant, registered professional engineer and leader in technology, mining, explosives and blasting science. Holding a BSC Hons Mining Engineering degree, management and explosive qualifications. Extensive experience in mining methods, education and management of projects and blast investigations. He has written papers, articles and presented at many International and Local conferences. Current board member of the IOQSA and regular contributions to the ISEE, EFEE, SAIMM & AusIMM organisations. With a strength in project design he led the AECI Blast Consult team with a strong passion for the development of environmental and blast monitoring, measurement and investigation, consulting, management and financial analysis.
For further information contact: Gugu Charlie, Conferences and Events Coordinator E-mail: gugu@saimm.co.za Tel: 011 538 0238 Web: www.saimm.co.za
HOIST & HAUL 2027
5-7 APRIL 2027 – CONFERENCE 8-9 APRIL 2027 – TECHNICAL VISIT
CENTURY CITY, CAPE TOWN In Partnership with The South African Institution of Mechanical Engineering and The Association of Mine Resident Engineers of South Africa.
FOUNDED 1894
THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY
OF TECHNICAL EXCELLENCE
ABOUT THE CONGRESS We are excited to be hosting Hoist & Haul 2027 in beautiful Cape town, South Africa. Mining is a global industry, with mining companies owning mines across the world. The large engineering companies and suppliers who provide design and equipment to the mines also operate internationally. So we hope to welcome delegates and accept technical papers from a wide spectrum of different countries. As the conference is being held in South Africa, we will also aim to highlight South African achievements in mine shaft hoisting, and technical visits will showcase excellence in current South African mining operations. Fifty years ago, the world, led by South Africa, was at the dawn of ultradeep mining. BMR hoists were being developed, accepted factors of safety were being challenged and updated on a more rational basis, much deeper single hoist shafts were replacing multiple shaft systems. These were exciting times for hoisting and gave birth to the Hoist and Haul series of conferences, in 1973 in Johannesburg, under the title “Hoisting: Men, Materials and Minerals”. Similar challenges exist today. Exploration has proved significant ore bodies at depths exceeding 4000 m. However, the cost and time required for extraction using current hoisting technology makes their exploitation unattractive, emphasizing the need for faster, more costefficient hoisting systems. But a whole new set of challenges has arisen from increasing societal demands for safety, sustainability, energy efficiency and environmental responsibility. And, whilst the world cannot develop and thrive without mining products, these demands are likely to escalate rapidly over the next fifty years. Mines that have been in operation for fifty years must upgrade and retrofit hoisting system to adapt to changing demands, and planning for new mines must aim to anticipate what will be necessary if they are to be socially acceptable from inception to closure in fifty years’ time.
FOR FURTHER INFORMATION, CONTACT: Gugu Charlie, Conferences and Events Coordinator
E-mail: gugu@saimm.co.za Tel:011 538 0238 Web: www.saimm.co.za
ECSA Validated CPD Activity, Credits = 1 point per full day attended
SAGC Validated Activity.