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Proceedings of PhD Student Poster Session 2014 Construction Research Congress Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham

This proceedings is invaluable to all practitioners and researchers in the field of construction engineering and management.

2014 CRC PhD Student Poster Session

2014 Construction Research Congress PhD Student Poster Sessions Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham Chair: Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign PhD Student Poster Session Organizing Committee Member: Youngjib Ham, University of Illinois at Urbana-Champaign CRC Executive Committee: SangHyun Lee, Ph.D., University of Michigan – Ann Arbor Amr Kandil, Purdue University Susan Bogus, University of New Mexico CRC2014 Conference Chair: Daniel Castro-Lacouture, Ph.D., Georgia Tech CRC2014 Technical Committee Co-Chairs: Baabak Ashuri, Ph.D., Georgia Tech Javier Irizarry, Ph.D., Georgia Tech The Technical Committee of CRC2014 PhD Student Poster Session: Alex Albert, Ph.D., North Carolina State University Amir Behzadan, Ph.D., Central Florida University Caroline Clevenger, Ph.D., Colorado State University Changbum Ahn, Ph.D., University of Nebraska- Lincoln Ken-Yu Liu, Ph.D., University of Washington Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign Ming Liu, Ph.D., University of Alberta Mounir El-Asmar, Ph.D., Arizona State University Pardis Pishdad-Bozorgi, Ph.D., Georgia Tech SangUk Han, Ph.D., University of Alberta Tanyel Bulbul, Ph.D., Virginia Tech Thais Alves, Ph.D., San Diego State University Xinyi Song, Ph.D., Georgia Tech The Members of the Jury– CRC2014 PhD Student Poster Session: Ali Touran, Ph.D., Northeastern University Carrie Sturts Dossick, Ph.D., University of Washington Charles Jahern, Ph.D., Iowa State University Daniel Castro-Lacouture, Ph.D., Georgia Tech Eddy Rojas, Ph.D., University of Nebraska- Lincoln Iris Tommelein, Ph.D., University of California – Berkeley Jesus M. de la Garza, Ph.D., Virginia Tech Miroslaw Skibniewski, University of Maryland – College Park Mohamed Al-Hussein, Ph.D., University of Alberta Simaan Abourizk, Ph.D., University of Alberta Page 1 of 84

List of Posters 1. RFID AND BIM-ENABLED WORKER LOCATION TRACKING TO SUPPORT REAL-TIME BUILDING PROTOCOL CONTROL AND DATA VISUALIZATION ON A LARGE HOSPITAL PROJECT ............................. 9 Aaron M. Costin (, Advisor: Dr. Jochen Teizer Georgia Institute of Technology 2. DEVELOPING CONTEXT SPECIFIC AND GENERALIZED CONSTRUCTION LABOUR PRODUCTIVITY MODELS ............................................................................................................................................................. 10 Abraham Assefa Tsehayae (tsehayae@ualberta .ca), Advisor: Dr. Aminah Robinson Fayek University of Alberta 3. AUTOMATED ASSESSMENT OF TORNADO-INDUCED BUILDING DAMAGE BASED ON LASER SCANNING ......................................................................................................................................................... 11 Alireza G. Kashani (, Advisor: Dr. Andrew J. Graettinger University of Alabama 4. COST EVALUATION MODEL FOR HOUSING RETROFIT DECISION-MAKING: A CASE STUDY ............. 12 Amirhosein Jafari (, Advisor: Dr. Vanessa Valentin University of New Mexico 5. SEGMENTATION AND NURBS FITTING OF UNORDERED BUILDING POINT CLOUDS .......................... 13 Andrey Dimitrov (, Advisors: Dr. Feniosky Pena Mora, Dr. Mani Golparvar-Fard Columbia University 6. SAVES II: A MULTIPLE SIGNALS ENHANCED AUGMENTED VIRTUALITY TRAINING SYSTEM FOR CONSTRUCTION HAZARD RECOGNITION ..................................................................................................... 14 Ao Chen (, Advisors: Dr. Brian Kleiner, Dr. Mani Golparvar-Fard Virginia Tech 7. QUANTIFYING ENERGY-USE BEHAVIOR IN COMMERCIAL BUILDINGS ................................................. 15 Ardalan Khosrowpour (, Rimas Gulbinas (, Advisor: Dr. John E. Taylor Virginia Tech 8. ADOPTION READINESS OF PREVENTION THROUGH DESIGN (PTD) CONTROLS IN CONCRETE, MASONRY, AND ASPHALT ROOFING ............................................................................................................. 16 Ari Goldberg (, Advisor: Dr. Deborah Young-Corbett Virginia Tech 9. A BIO-INSPIRED VIRTUAL PEDAGOGICAL ENVIRONMENT TO STIMULATE BIO-INSPIRED THINKING ............................................................................................................................................................................ 17 Aruna Muthumanickam (, Advisor: Dr. John E. Taylor Virginia Tech 10. OPTIMIZING THE SUSTAINABILITY OF SINGLE-FAMILY HOUSING UNITS ........................................... 18 Aslihan Karatas (, Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign 11. THREE-TIERED DATA & INFORMATION INTEGRATION FRAMEWORK FOR HIGHWAY PROJECT DECISION- MAKINGS ........................................................................................................................................ 19 Asregedew Woldesenbet (, Advisor: Dr. “David” Hyung Seok Jeong Iowa State University 12. MINIMIZING EFFECTS OF OVERFITTING AND COLLINEARITY IN CONSTRUCTION COST ESTIMATION: A NEW HYBRID APPROACH ..................................................................................................... 20 Bo Xiong (, Advisor: Dr. Martin Skitmore, Dr. Bo Xia Queensland University of Technology

2014 CRC PhD Student Poster Session

13. AUTOMATED REAL-TIME TRACKING AND 3D VISUALIZATION OF CONSTRUCTION EQUIPMENT OPERATION USING HYBRID LIDAR SYSTEM ................................................................................................. 21 Chao Wang (, Advisor: Dr. Yong K. Cho Georgia Institute of Technology 14. INTERDEPENDENT INFRASTRUCTURE NETWORK SYSTEM VULNERABILITY IDENTIFICATION ..... 22 Christopher Van Arsdale (, Advisor: Dr. Amlan Mukherjee Michigan Technological University 15. VOLATILE ORGANIC COMPOUNDS EMISSIONS GENERATED IN HOT-MIX ASPHALT PAVEMENT CONSTRUCTION AND THEIR HEALTH EFFECTS ON PAVEMENT WORKERS ........................................... 23 Dan Chong (, Advisor: Dr. Yuhong Wang The Hong Kong Polytechnic University 16. QUANTITATIVE PERFORMANCE ASSESSMENT OF SINGLE-STEP AND TWO-STEP DESIGN-BUILD PROCUREMENT ................................................................................................................................................ 24 David Ramsey (, Advisor: Dr. Mounir El Asmar, Dr. G. Edward Gibson Arizona State University 17. RISK ALLOCATION IN PUBLIC-PRIVATE PARTNERSHIPS: ANALYSIS OF CONTRACTUAL PROVISIONS IN 18 U.S. HIGHWAY PROJECTS .............................................................................................. 25 Duc A. Nguyen ( and Edwin Gonzalez (, Advisor: Dr. Michael J. Garvin Virginia Tech 18. EXTENDING BUILDING INFORMATION MODELING (BIM) INTEROPERABILITY TO GEO-SPATIAL DOMAIN USING SEMANTIC WEB TECHNOLOGY .......................................................................................... 26 Ebrahim P. Karan (, Advisor: Javier Irizarry Georgia Institute of Technology 19. QUANTIFYING THE RISKS OF WILDFIRE TO BUILDINGS IN WILDLAND URBAN INTERFACE: A FORWARD VIEW ............................................................................................................................................... 27 Elmira Kalhor (, Advisor: Dr. Vanessa Valentin University of New Mexico 20. COLLABORATION THROUGH INNOVATION: A MULTI-LAYERED FRAMEWORK FOR THE AECM INDUSTRY .......................................................................................................................................................... 28 Erik A. Poirier (, Advisor: Dr. Daniel Forgues, Dr. Sheryl Staub-French École de Technologie Supérieure 21. THE VIRTUAL CONSTRUCTION SIMULATOR: AN EDUCATIONAL GAME IN CONSTRUCTION ENGINEERING ................................................................................................................................................... 29 Fadi Castronovo (, Advisor: Dr. John I. Messner The Pennsylvania State University 22. PREDICTIVE EMISSIONS MODELS FOR EXCAVATORS ......................................................................... 30 Heni Fitriani (, Advisor: Dr. Phil Lewis Oklahoma State University 23. ESTIMATING EXTREME EVENT RECOVERY WITH CONSTRUCTION ACTIVITY CHANGE POINTS ... 31 Henry D. Lester (, Advisor: Dr. Gary P. Moynihan University of Alabama 24. AN INTEGRATED SIMULATION AND OPTIMIZATION BASED RESIDENTIAL CONSTRUCTION CARBON FOOTPRINT AND EMISSION ASSESSMENT .................................................................................. 32 Hong Xian Li (, Advisor: Dr. Mohamed Al-Hussein, Dr. Mustafa Gül University of Alberta Page 1 of 84

2014 CRC PhD Student Poster Session

25. 3D RECONSTRUCTION OF INDUSTRIAL EQUIPMENT USING COMBINED GEOMETRIC AND TOPOLOGICAL INFORMATION FROM LASER-SCANNED DATA .................................................................. 33 Hyojoo Son (, Advisor: Dr. Changwan Kim Chung-Ang University 26. INFORMATION EXTRACTION AND AUTOMATED REASONING FOR AUTOMATED REGULATORY COMPLIANCE CHECKING IN THE CONSTRUCTION DOMAIN ...................................................................... 34 Jiansong Zhang (, Advisor: Dr. Nora El-Gohary University of Illinois at Urbana-Champaign 27. EX-ANTE ASSESSMENT OF PERFORMANCE IN CONSTRUCTION PROJECTS: A SYSTEM-OFSYSTEMS APPROACH ...................................................................................................................................... 35 Jin Zhu (, Advisor: Dr. Ali Mostafavi Florida International University 28. A FRAMEWORK FOR PUBLIC PRIVATE PARTNERSHIP RISK MITIGATION IN RURAL POST CONFLICT ENVIRONMENTS– A SYSTEMS APPROACH ............................................................................... 36 John T. Mitchell (, Advisor: Dr. Yvan Beliveau Virginia Tech 29. FRAMEWORK FOR ON-SITE BIOMECHANICAL ANALYSIS DURING CONSTRUCTION TASKS ........... 37 JoonOh Seo (, Advisor: Dr. SangHyun Lee University of Michigan 30. DEVELOP A PRICE ESCALATION METHOD FOR SINGLE AWARD INDEFINITE DELIVERY/INDEFINITE QUANTITY CONTRACTS: AXE BIDDING ......................................................................................................... 38 Jorge A. Rueda (, Advisor: Dr. Douglas D. Gransberg Iowa State University 31. CONSTRUCTION OPERATIONS AUTOMATION USING MODIFIED DISCRETE EVENT SIMULATION MODELS ............................................................................................................................................................. 39 Joseph Louis (, Advisor: Dr. Phillip S. Dunston Purdue University 32. AUTONOMOUS NEAR-MISS FALL ACCIDENT DETECTION TECHNIQUE USING INERTIAL MEASUREMENT UNITS ON CONSTRUCTION IRON-WORKERS .................................................................. 40 Kanghyeok Yang ( and Sepideh S. Aria (, Advisor: Dr. Changbum Ahn University of Nebraska at Lincoln 33. MANAGING WATER AND WASTEWATER INFRASTRUCTURE IN SHRINKING CITIES ......................... 41 Kasey Faust (, Advisor: Dr. Dulcy Abraham Purdue University 34. MONITORING CONSTRUCTION PROGRESS AT THE OPERATION-LEVEL USING 4D BIM AND SITE PHOTOLOGS ..................................................................................................................................................... 42 Kevin K. Han (, Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign 35. ESTIMATING OPTIMAL LABOR PRODUCTIVITY: A TWO-PRONG STRATEGY ...................................... 43 Krishna Kisi ( and Nirajan Mani (, Advisor: Dr. Eddy Rojas University of Nebraska-Lincoln 36. AN INVESTIGATION OF OCCUPANT ENERGY USE BEHAVIOR AND INTERVENTIONS IN A RESIDENTIAL CONTEXT .................................................................................................................................. 44 Kyle Anderson (, Advisor: Dr. SangHyun Lee Page 2 of 84

2014 CRC PhD Student Poster Session

University of Michigan 37. MEASURING THE COMPLEXITY OF MEGA CONSTRUCTION PROJECTS IN CHINA—A FUZZY ANALYTIC NETWORK PROCESS ..................................................................................................................... 45 Lan Luo, Advisor: Dr. Qinghua He Tongji University 38. DECISION SUPPORT SYSTEM FOR SUSTAINABLE LABOR MANAGEMENT IN MASONRY CONSTRUCTION ............................................................................................................................................... 46 Laura Florez (, Advisor: Dr. Daniel Castro-Lacouture Georgia Institute of Technology 39. BIM-BASED INTEGRATED APPROACH FOR OPTIMIZED CONSTRUCTION SCHEDULING UNDER RESOURCE CONSTRAINTS ............................................................................................................................. 47 Hexu Liu (, Advisor: Dr. Mohamed Al-Hussein, Dr. Ming Lu University of Alberta 40. INCREASING MINDFULNESS OF COORDINATION PRACTICES IN INNER CITY UTILITY PROJECTS: THE ROLE OF NEW (BIM) TECHNOLOGIES ................................................................................................... 48 Léon L. olde Scholtenhuis (, Advisor: Dr. T. Hartmann University of Twente 41. A SYSTEMATIC RISK ANALYSIS APPROACH AGAINST TUNNEL-INDUCED BUILDING DAMAGES .... 49 Limao Zhang (, Advisor: Dr. Xianguo Wu Huazhong University of Science and Technology 42. MODELING AND VISUALIZING THE FLOW OF TRADE CREWS IN CONSTRUCTION USING AGENTS AND BUILDING INFORMATION MODELS (BIM) .............................................................................................. 50 Lola Ben-Alon (, Advisor: Dr. Rafael Sacks Technion IIT 43. MEASURING INTERDEPENDENT INFRASTRUCTURE RESILIENCE UNDER NORMAL AND EXTREME CONDITIONS ...................................................................................................................................................... 51 María E. Nieves-Meléndez (, Advisor: Dr. Jesús M. de la Garza Virginia Tech 44. THERMALLY ACTIVATED CLAY BASED BIOMASS POZZOLANA INVESTIGATIONS FOR SUSTAINABLE CONSTRUCTION IN GHANA ................................................................................................... 52 Mark Bediako (, Advisor: SKY Gawu and AA Adjaottor Kwame Nkrumah University of Science and Technology, Ghana 45. ASSESSMENT OF ACTIVITIES’ CRITICALITY TO CASH-FLOW PARAMETERS ..................................... 53 Marwa Hussein Ahmed (, Advisor: Dr. Tarek Zayed, Dr. Ashraf Elazouni Concordia University 46. UNDERSTANDING CURRENT HORIZONTAL DIRECTIONAL DRILLING PRACTICES IN MAINLAND CHINA BEING USED FOR ENERGY PIPELINE CONSTRUCTION .................................................................. 54 Maureen Cassin (, Advisor: Dr. Samuel Ariaratnam Arizona State University 47. OPTIMIZING THE SELECTION OF SUSTAINABILITY MEASURES FOR EXISTING BUILDINGS ............ 55 Moatassem Abdallah (, Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign 48. COMPETENCIES AND PERFORMANCE IN CONSTRUCTION PROJECTS ............................................. 56 Moataz Nabil Omar (, Advisor: Dr. Aminah Robinson Fayek University of Alberta Page 3 of 84

2014 CRC PhD Student Poster Session

49. IMPROVING CONSTRUCTION COST ESCALATION ESTIMATION USING MACROECONOMIC, ENERGY AND CONSTRUCTION MARKET VARIABLES ................................................................................. 57 Mohsen Shahandashti (, Advisor: Dr. Baabak Ashuri Georgia Institute of Technology 50. EX-ANTE SIMULATION AND VISUALIZATION OF SUSTAINABILITY POLICIES IN INFRASTRUCTURE SYSTEMS: A HYBRID METHODOLOGY FOR MODELING AGENCY-USER-ASSET INTERACTIONS .......... 58 Mostafa Batouli (, Advisor: Dr. Ali Mostafavi Florida International University 51. DYNAMIC FATIGUE MODEL FOR ASSESSING MUSCLE FATIGUE DURING CONSTRUCTION TASKS ............................................................................................................................................................................ 59 MyungGi Moon ( Advisor: Dr. SangHyun Lee University of Michigan 52. TOWARD SUSTAINABLE CAPITAL TRANSPORTATION INFRASTRUCTURE: MAXIMIZING PERFORMANCE OF PREPLANNING PHASE .................................................................................................. 60 Nahid Vesali (, Advisor: Dr. Mehmet Emre Bayraktar Florida International University 53. A QUANTITATIVE INVESTIGATION OF BUILDING MICRO-LEVEL POWER MANAGEMENT THROUGH ENERGY HARVESTING FROM OCCUPANT MOBILITY .................................................................................. 61 Neda Mohammadi (, Advisor: Dr. Tanyel Bulbul, Dr. John E. Taylor Virginia Tech 54. ESTIMATING LABOR PRODUCTIVITY FRONTIER: A PILOT STUDY ....................................................... 62 Nirajan Mani ( and Krishna P. Kisi (, Advisor: Dr. Eddy M. Rojas University of Nebraska-Lincoln 55. A DECISION SUPPORT SYSTEM FOR SUSTAINABLE MULTI OBJECTIVE ROADWAY ASSET MANAGEMENT .................................................................................................................................................. 63 Omidreza Shoghli (, Advisor: Dr. Jesus M. de la Garza Virginia Tech 56. SIMULEICON: A SIMULATION-BASED MULTI-OBJECTIVE DECISION-SUPPORT TOOL FOR SUSTAINABLE BUILDING DESIGN ................................................................................................................... 64 Peeraya Inyim (, Advisor: Dr. Yimin Zhu, Dr. Wallied Orabi Florida International University 57. CONSTRUCTION OPERATIONS PROCESS DATA MODELING AND KNOWLEDGE DISCOVERY USING MACHINE LEARNING CLASSIFIERS ................................................................................................................ 65 Reza Akhavian (, Advisor: Dr. Amir H. Behzadan University of Central Florida 58. THE DEVELOPMENT OF AN AUTOMATED PROGRESS MONITORING AND CONTROL SYSTEM FOR CONSTRUCTION PROJECTS ........................................................................................................................... 66 Reza Maalek (, Advisor: Dr. Janaka Ruwanpura, Dr. Derek Lichti University of Calgary 59. OPTIMUM RESOURCE UTILIZATION PLANNING IN CONSTRUCTION PORTFOLIOS THROUGH MODELING OF EVERYDAY UNCERTAINTIES AT CERTAIN CONFIDENCE LEVEL ..................................... 67 Reza Sheykhi (, Advisor: Dr. Wallied Orabi Florida International University 60. USING STEP APPROACH TO ACHIEVE SUCCESSFUL OUTCOMES ON COMPLEX PROJECTS ........ 68 Ron Patel (, Advisor: Dr. Edward J. Jaselskis Page 4 of 84

2014 CRC PhD Student Poster Session

North Carolina State University 61. QUANTIFYING HUMAN MOBILITY PERTURBATION UNDER THE INFLUENCE OF TROPICAL CYCLONES ........................................................................................................................................................ 69 Qi Wang (, Advisor: Dr. John E. Taylor Virginia Tech 62. CONSTRUCTION WORKERS’ BEHAVIOR INFLUENCED BY SOCIAL NORMS: A STUDY OF WORKERS’ BEHAVIOR USING AGENT-BASED SIMULATION INTEGRATED WITH EMPIRICAL METHODS ............................................................................................................................................................................ 70 Seungjun Ahn (, Advisor: Dr. SangHyun Lee University of Michigan 63. CONSTRUCTION SITE LAYOUT PLANNING USING SIMULATION .......................................................... 71 SeyedReza RazaviAlavi (, Advisor: Dr. Simaan AbouRizk University of Alberta 64. 4-DIMENSIONAL PROCESS-AWARE SITE-SPECIFIC CONSTRUCTION SAFETY PLANNING .............. 72 Sooyoung Choe (, Advisor: Dr. Fernanda Leite The University of Texas at Austin 65. THE IMPACT OF BUSINESS-PROJECT INTERFACE ON CAPITAL PROJECT PERFORMANCE ........... 73 Sungmin Yun (, Advisor: Dr. Stephen P. Mulva, Dr. William J. O’Brien University of Texas at Austin 66. EXPLORING A PREFERENTIAL FRAMEWORK FOR FUTURE PROJECT OPPORTUNITIES ................ 74 Timothy W. Gardiner (, Advisor: Dr. Yvan J. Beliveau Virginia Tech 67. ENVISIONING MORE SUSTAINABLE INFRASTRUCTURE THROUGH CHOICE ARCHITECTURE ........ 75 Tripp Shealy (, Advisor: Dr. Leidy Klotz Clemson University 68. SEGMENTATION AND RECOGNITION OF ROADWAY ASSETS FROM CAR-MOUNTED CAMERA VIDEO STREAMS USING A SCALABLE NON-PARAMETRIC IMAGE PARSING METHOD ........................... 76 Vahid Balali (, Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign 69. INTEGRATED COMPUTATIONAL MODEL IN SUPPORT OF VALUE ENGINEERING ............................. 77 Yalda Ranjbaran (, Advisor: Dr. Osama Moselhi Concordia University 70. IMPROVING CAMPUS BUILDING ENERGY EFFICIENCY AND OCCUPANTS SATISFACTION THROUGH APPLICATION OF ARTIFICIAL INTELLIGENCE INTO CAMPUS FACILITY MANAGEMENT ...... 78 Yang Cao (, Advisor: Dr. Xinyi Song Georgia Institute of Technology 71. A BIO-INSPIRED SOLUTION TO MITIGATE URBAN HEAT ISLAND EFFECTS ....................................... 79 Yilong Han (, Advisor: Dr. John E. Taylor Virginia Tech 72. FORECASTING LONG-TERM STAFFING REQUIREMENTS FOR STATE TRANSPORTATION AGENCIES .......................................................................................................................................................... 80 Ying Li (, Advisor: Dr. Timothy R. B. Taylor University of Kentucky

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2014 CRC PhD Student Poster Session

73. VISION-BASED BUILDING ENERGY DIAGNOSTICS AND RETROFIT ANALYSIS USING 3D THERMOGRAPHY AND BIM ............................................................................................................................. 81 Youngjib Ham (, Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign 74. MULTI-TIERED SELECTION OF PROJECT DELIVERY SYSTEMS FOR CAPITAL PROJECTS .............. 82 Zorana Popić (, Advisor: Dr. Osama Moselhi Concordia University

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2014 CRC PhD Student Poster Session

List of Participants Student Name


Advisor Name


Aaron Costin

Georgia Institute of Technology

Jochen Teizer


Abraham Tsehayae

University of Alberta

Aminah Robinson Fayek


Alireza Geranmayeh Kashani

University of Alabama

Andrew J. Graettinger


Amirhosein Jafari

University of New Mexico


Andrey Dimitrov

Columbia University


Ao Chen

Virginia Tech

Vanessa Valentin Feniosky Pena Mora, Mani Golparvar-Fard Brian Kleiner, Mani Golparvar-Fard


Ardalan Khosrowpour

Virginia Tech

John E. Taylor


Ari Goldberg

Virginia Tech

Deborah Young-Corbett


Aruna Muthumanickam

John E. Taylor


Aslihan Karatas


Asregedew Woldesenbet


Bo Peter Xiong


Chao Wang


Christopher Van Arsdale


Dan Chong

Virginia Tech University of Illinois at UrbanaChampaign Iowa State University Queensland University of Technology Georgia Institute of Technology Michigan Technological University The Hong Kong Polytechnic University


David Ramsey

Arizona State University


Duc Nguyen & Edwin Gonzales

Virginia Tech

Mounir El Asmar, G. Edward Gibson Michael J. Garvin


Ebrahim Karan

Georgia Institute of Technology

Javier Irizarry


Elmira Kalhor

University of New Mexico


Erik Poirier

École de Technologie Supérieure

Vanessa Valentin Daniel Forgues, Sheryl StaubFrench


Fadi Castronovo


Heni Fitriani

The Pennsylvania State University Oklahoma State University


Henry D. Lester

University of Alabama

Gary P. Moynihan


Hong Li

University of Alberta

Mohamed Al-Hussein, Mustafa Gül


Hyojoo Son

Changwan Kim


Jiansong Zhang


Jin Zhu

Chung-Ang University University of Illinois at UrbanaChampaign Florida International University


John Mitchell

Virginia Tech

Yvan Beliveau


JoonOh Seo

University of Michigan

SangHyun Lee


Jorge A. Rueda

Iowa State University

Douglas D. Gransberg


Purdue University

Phillip S. Dunston

University of Nebraska at Lincoln

Changbum Ahn


Joseph Louis Kanghyeok Yang & Sepidek S. Aria Kasey Faust

Dulcy Abraham


Kevin Han


Krishna Kisi

Purdue University University of Illinois at UrbanaChampaign University of Nebraska-Lincoln


Khaled El-Rayes “David” Hyung Seok Jeong Martin Skitmore, Bo Xia Yong K. Cho Amlan Mukherjee Yuhong Wang

John I. Messner Phil Lewis

Nora El-Gohary Ali Mostafavi

Mani Golparvar-Fard Eddy Rojas Page 7 of 84

2014 CRC PhD Student Poster Session


Kyle Anderson

University of Michigan

SangHyun Lee


Lan Luo

Tongji University

Qinghua He


Laura Florez

Georgia Institute of Technology

Daniel Castro-Lacouture


Lio Liu

University of Alberta

Mohamed Al-Hussein, Ming Lu


Léon L. olde Scholtenhuis

T. Hartmann


Limao Zhang


Lola Ben-Alon

University of Twente Huazhong University of Science and Technology Technion IIT


Maria Nieves

Jesús M. de la Garza


Mark Bediako


Marwa Hussien

Virginia Tech Kwame Nkrumah University of Science and Technology Concordia University


Maureen Cassin

Samuel Ariaratnam


Moatassem Abdallah


Moataz Omar

Arizona State University University of Illinois at UrbanaChampaign University of Alberta


Mohsen Shahandashti

Georgia Institute of Technology

Baabak Ashuri


Mostafa Batouli

Florida International University

Ali Mostafavi


MyungGi Moon

University of Michigan

SangHyun Lee


Nahid Vesali Mahmoud

Florida International University

Mehmet Emre Bayraktar


Neda Mohammadi

Virginia Tech

Tanyel Bulbul, John E. Taylor


Nirajan Mani

University of Nebraska-Lincoln

Eddy M. Rojas


Omidreza Shoghli

Virginia Tech

Jesus M. de la Garza


Peeraya Inyim

Florida International University

Yimin Zhu, Wallied Orabi


Reza Akhavian

University of Central Florida

Amir H. Behzadan


Reza Maalek

University of Calgary

Janaka Ruwanpura, Derek Lichti


Reza Sheykhi

Florida International University

Wallied Orabi


Ron Patel

North Carolina State University

Edward J. Jaselskis


Ryan Qi Wang

Virginia Tech

John E. Taylor


Seungjun Ahn

University of Michigan

SangHyun Lee


SeyedReza RazaviAlavi

University of Alberta

Simaan AbouRizk


Sooyoung Choe

University of Texas at Austin


Sungmin Yun

University of Texas at Austin


Timothy W. Gardiner

Virginia Tech

Fernanda Leite Stephen P. Mulva, William J. O’Brien Yvan J. Beliveau


Tripp Shealy

Leidy Klotz


Vahid Balali


Yalda Ranjbaran

Clemson University University of Illinois at UrbanaChampaign Concordia University


Yang Cao

Georgia Institute of Technology

Xinyi Song


Yilong Han

Virginia Tech

John E. Taylor


Ying Li

Timothy R. B. Taylor


Youngjib Ham


Zorana Popić

University of Kentucky University of Illinois at UrbanaChampaign Concordia University

Xianguo Wu Rafael Sacks

SKY Gawu and AA Adjaottor Tarek Zayed, Ashraf Elazouni

Khaled El-Rayes Aminah Robinson Fayek

Mani Golparvar-Fard Osama Moselhi

Mani Golparvar-Fard Osama Moselhi Page 8 of 84

2014 CRC PhD Student Poster Session

1. RFID and BIM-Enabled Worker Location Tracking to Support Real-time Building Protocol Control and Data Visualization on a Large Hospital Project Aaron M. Costin (, Advisor: Dr. Jochen Teizer Georgia Institute of Technology As construction job sites get larger and more complex, the need to increase building protocol control and security is becoming more necessary. Having a real-time tracking system for materials, equipment and personnel installed on a job site will help project managers to enhance the security, safety, quality control, and worker logistics of a construction project. This research presents the method of integrating passive Radio Frequency Identification (RFID) and Building Information Modeling (BIM) for real-time tracking of personnel, material, and equipment. The main purpose is to generate real-time data to monitor for safety, security, and worker logistics, as well as to produce leading indicators for safety and building protocol control. The concept of reference tags will be utilized along with a cloud server, mobile field devices, and software to assist the project managers with staying connected with the job site, from supply chain management to installation. Hardware components include passive RFID tags, portal RFID readers, fixed turn-style readers, and mobile handheld devices. The system was deployed on a 900,000 square feet hospital project that consisted of three major buildings, 125 contractors, and 1,200 workers. Preliminary results show that the integration of these technologies enhances productivity, reduces scheduling issues, assists in subcontractor management, and provides real-time information on deployed crews and building activities. High-level metrics have been developed at the project and large contractor level. Additionally, the system also provided real-time information on local worker participation as part of the project goal. Significantly, based on experimental analysis, it is demonstrated that the RFID and BIM system is a practical and resourceful tool to provide real-time information and location tracking to increase safety, security, and building protocol control.

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2. Developing Context Specific and Generalized Construction Labour Productivity Models Abraham Assefa Tsehayae (, Advisor: Dr. Aminah Robinson Fayek University of Alberta Construction labour productivity (CLP), which measures the efficiency of construction labourers in converting a given set of inputs, such as materials and equipment, into tangible outputs, is one significant issue shaping the viability of undertaking construction projects in Canada due to its substantial and direct impact on project costs. Unfortunately, Albertan construction labour productivity at economic level shows a declining trend. This decline together with shortage of labour supply in the nation and particularly in the province of Alberta has threatened the future of investment in construction and optimizing CLP through appropriate analysis and modeling is therefore past critical. However, modeling CLP is a challenge as the input variables (factors and practices) influencing it are numerous, complex, dynamic, and inconsistent from project to project. Thus, an integrated and flexible approach to develop and adapt CLP models to suit different project contexts is not yet achieved. This PhD poster presents the established research framework for developing CLP models based on a system based granular computing approach so as to addresses the stated limitations. The focus of the research in terms of the objective and proposed system model based on input (key factors), process (work sampling proportions), and output (CLP) variables is discussed. Development of context specific CLP models, adapting developed CLP models to suit varying contexts, and abstracting context specific models to a generalized CLP model is also presented. Initial findings on the identification of key input variable categories and preliminary data analysis of the relationship between process variables (work sampling proportions) and labour productivity are reported. As its final outcomes, the study will establish multilevel critical factors and practices for improved construction planning and execution and provide industry with an advanced prediction tool for use in construction planning and project control.

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3. Automated Assessment of Tornado-Induced Building Damage Based on Laser Scanning Alireza G. Kashani (, Advisor: Dr. Andrew J. Graettinger University of Alabama The assessment of damage sustained by built infrastructure after natural disasters is of importance to analyze loads and mechanisms of structural failures in order to improve design and construction methods. It is also required to prepare loss estimates in order to manage disaster assistance programs and reconstruction efforts. Perishable damage data should be appropriately recorded, and investigations should be completed in a timely manner in order to avoid interfering with clean up and recovery efforts that quickly change damage sites. 3D laser scanning is an effective technology that enables acquiring geometric information of damaged buildings with high precision. The dense 3D point cloud data produced by laser scanning virtually reconstructs the damage site and enables engineers to take measurements and retrieve geometric information needed for damage assessment. Such information is vital to analyze structural damage and estimate loss. However, manual taking measurements and processing data are challenging and time consuming due to the large size of data collected and repetitive manual operations and calculations. This PhD research aims at developing a data processing framework to automatically analyze tornado-induced building damage based on laser scanning data. A GIS-enabled data processing framework was developed to automatically detect damaged roof and wall surfaces in scans of buildings and generate GIS damage and wind speed maps. This framework also enables to retrieve geographic information including the percentage of roof/wall damage, the roof pitch, the distance and orientation of roof/wall surface in respect to tornado path, etc. Performance of data processing framework was tested with simulated data, laboratory scans, and actual data collected after tornadoes. Roof models with controlled extent of damage were made and scanned with controlled settings. Also, simulated point clouds of damaged buildings with controlled settings and complexities were generated. These point cloud datasets were used to objectively evaluate accuracy of algorithms and examine impacts of data- and algorithm-related factors such as point cloud density, extent of damage, color of roof shingles, etc. Therefore, optimum laser scanning and algorithm settings were identified. These analyses determined that for typical point cloud density (>25 points/m2), proposed algorithms resulted in less than 10% error in calculating percentages of roof and wall damage. The proposed framework was also tested with actual damage data collected after the Tuscaloosa, AL and Moore, OK tornadoes. The developed framework calculated roof/wall loss and estimated wind speeds at finer scales than the typical large-scale assessments done by reconnaissance engineers. The damage assessment framework developed in this research can be adopted for practical applications in construction industry including:  Automated generation of damage maps for disaster response and recovery  Automated loss database updating for management of insurance and disaster assistance programs  Automated preliminary loss estimation for cost analysis of repair and reconstruction projects

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4. Cost Evaluation Model for Housing Retrofit Decision-Making: A Case Study Amirhosein Jafari (, Advisor: Dr. Vanessa Valentin University of New Mexico Since over 60% of the housing inventory in the United States is more than 30 years old, the refurbishment and retrofitting of old homes is becoming an important issue. The retrofitting of existing homes can have a positive impact on the environment if methods to conserve energy and natural resources are considered. Even though there are numerous resources for providing advice on how to retrofit a facility, it is a complex decision to select optimum combination of retrofitting activities for a specific building. Two of the main concerns of housing retrofits are how much money the owner needs to invest and how best to use this investment to ensure an optimum return. A typical solution to these problems is to evaluate different possible retrofitting alternatives conducting a life cycle cost analysis (LCCA). The decision-maker can then select a plan with minimum life cycle cost (LCC) as the best housing retrofit strategy. However, it would be a hard and time consuming process to perform a LCCA for all possible alternatives for each building. The objective of this study is to introduce a simple approach for evaluating housing retrofit alternatives, using a real retrofitting case study as a benchmark. First, a detailed LCCA is performed for implementing a combination of 15 different retrofitting activities - varying from low to high cost efforts - to the case of a house built in 1950’s in Albuquerque, New Mexico. After that, assuming that investment of retrofitting costs will reduce energy consumption cost of the building, an approach is developed to illustrate the trend of retrofitting cost and energy consumption saving of different housing retrofit plans for the studied case. The initial results illustrate that the retrofitting plan with minimum LCC for the studied case needs $36,575 investment which may cause decrease in annual utility costs to 15% for life cycle of 50 years. The results also show that by increasing the life cycle of the project, the optimum LCC retrofitting plan approaches to Net Zero Energy (NZE) strategy. Using the developed approach and according to the housing retrofit case study as a benchmark, this study develops a model to evaluate the effectiveness of retrofitting efforts according to the cost and environmental issues. The developed model can aid project owners in evaluating their existing retrofitting projects according to the project cost and energy savings. Further, the results provide guidance for decision-makers to how much money it is preferred to invest for a new retrofitting project.

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2014 CRC PhD Student Poster Session

5. Segmentation and NURBS Fitting of Unordered Building Point Clouds Andrey Dimitrov (, Advisors: Dr. Feniosky Pena Mora, Dr. Mani Golparvar-Fard Columbia University 3D modeling of the built environment is used in a variety of civil engineering analysis scenarios. Significant applications include generating 3D models for the real-estate industry, building renovation projects, and inspection of fabrication and on-site assemblies during construction. This data can originate from structured light methods, mainly laser scanners, or 3D reconstruction methods using images or videos. The process of generating 3D models from point cloud data involves manually identifying collections of points that belong to each surface, and then fitting geometry (e.g., meshes, primitives, NURBS, subdivision) to them. Despite several existing systems that assist with semiautomated segmentation and placement of architectural elements and building systems such as pipes and ducts, the process is still primarily manual, painstaking, and requires expertise. The complexity of the task in hand produces significant technical challenges attributed to: 1) Density: Point clouds exhibit locally variable densities as well as missing data due to occlusions; 2) Surface Roughness: The physical texture of common surfaces can range from smooth (steel, marble) to very irregular (grass, crushed stone) within a single scene, thus no priors about noise levels can be reliably used; 3) Curvature: Surfaces can be flat, single curved, double curved, or have undulations at multiple scales, making boundaries hard to define; 4) Clutter: A scene can be made up of multiple objects in close proximity, making feature detection difficult; and 5) Abstraction: 3D modeling as an abstraction process, requires some decisions to be made by the user. In consequence, automation needs to balance flexibility with the ease of use. The over-arching objective of this work is to create and validate a new method for creating semantically rich CAD models from point cloud data. We address the technical research challenges by proposing a segmentation and NURBS fitting method for automated modeling. In our method, the user sets a single parameter that accounts for the desired level of abstraction. We treat this parameter as a locally adaptive threshold, allowing segmentation to account for local context. Segmentation starts with a multi-scale feature detection step, describing surface roughness and curvature around each 3D point, followed by seed finding and region growing steps. We then successively fit uniform B-spline curves in 2D as planar cross sectional cuts on the surface. An intermediate B-Spline surface is then computed by globally optimizing the cross sections and lofting over the cross sections. The final NURBS surface for each segment is computed iteratively as a refinement of the intermediate surface. We also present a new benchmark of incomplete and noisy point clouds assembled from a variety of architectural/construction scenes, together with their humangenerated segmentations and NURBS surfaces, which we treat as ground truth. We then compute metrics that measure how well the computer-generated segmentations and NURBS surfaces match the human-generated ones, and compare the performance of the state-of-the-art method. The ground truth in our experiments was manually generated from point cloud scenes. Segments were defined based on experts’ judgment of surface continuity and not object completeness. We introduce seven metrics on accuracy and completeness for comparison of segmentation and NURBS fitting algorithms. By comparing results with the state-of-the-art methods using these metrics, our method shows reliable performance for both segmentation and NURBS fitting in challenging point clouds. Our contributions are three-fold: First, we present a region-growing algorithm based on multi-scale geometrical features that treats the desired level of modeling abstraction as a locally adaptive parameter. Second, we introduce a NURBS fitting method that accounts for all the topological variations and reliably maps physical space to parameter space given unordered, incomplete, and noisy point clouds. Third, we present metrics and perform quantitative comparisons of our method with the human-generated segmentations, and provide a publicly available dataset for future analysis and comparison of point cloud segmentation. Page 13 of 84

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6. SAVES II: A Multiple Signals Enhanced Augmented Virtuality Training System for Construction Hazard Recognition Ao Chen (, Advisors: Dr. Brian Kleiner, Dr. Mani Golparvar-Fard Virginia Tech Safety training arises its importance in construction in recent two decades but it doesn't approach the full expectations in practice as it supposed to. High number of fatalities and injuries occur every year that push those companies which treat safety as their core value more eager to improve the effort of safety training. Accompany with the rapid growth of IT innovation in current years, Augmented Virtuality (AV) presents its potential in construction safety training. Comparing with Augmented Reality and Virtual Reality, AV brings high quality of realistic telepresence and enhanced power of synthetic imagery experience without exposing the workers to the fully hazardous training site. SAVES, an AV based strategy for training construction hazard recognition is developed and applied to improve the workers’ safety awareness and hazard recognition skills in order to approach the desired expectations of best safety program. SAVES has showed its effectiveness and benefits in the field test but there is a need to better understand 1) whether different safety programs and multiple signals can be synthesized for hazards perceiving and 2) such plentiful information stream can help to maximize hazard recognition skills in workers. Thus, SAVES II is developed to answer such questions. Besides the integrated BIM, photographs of typical energy sources, built-in augmented training scenarios and interactive avatar that developed in SAVES, the traditional training methods such as lectures, videos and power points are enhanced in SAVES II in order to response to the questions. Furthermore, multiple enriched signals such as visual signals, audio signals and difficult levels are also developed with the purpose of testing how worker’s working memory perceives the information with such multisignals and helps to improve hazard recognition skills in long term memory. The modeling process, analysis and the current lessons learned are discussed later.

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2014 CRC PhD Student Poster Session

7. Quantifying Energy-Use Behavior in Commercial Buildings Ardalan Khosrowpour (, Rimas Gulbinas (, Advisor: Dr. John E. Taylor Virginia Tech Buildings account for more than 40% of CO2 emissions in the United States and recent research has indicated that CO2 emissions of commercial buildings are expected to increase faster than all other types of buildings at an annual rate of 1.8%. Recent advances in building technologies and materials, construction methods, building monitoring and control systems, and other similar areas has enabled the development and management of increasingly energy efficient environments. However, technology independent elements such as the behavior of building occupants remain significant factors responsible for energy consumption and associated CO2 emissions of these buildings. In this research, we illustrate a novel approach to quantify and target inefficient occupants’ energy consumption. The primary objective is to classify commercial building occupants based on their energy consumption pattern. The secondary objective is to design a new metric for building occupant energyuse entropy based on energy consumption pattern consistency. We developed an algorithm that implements clustering and machine learning methods along with various entropy measures to categorize the occupants, days, and hours based on energy consumption rates and patterns. Using real-time high resolution wireless power meters, we conducted an energy monitoring experiment in a commercial building involving approximately 100 participants located in Denver, CO. The data from this experiment were used to develop the models and to assign occupants with similarly patterned energy consumption into categories. This novel methodology executes as a powerful proxy to ease the energy prediction procedure for commercial building occupants. More importantly, it also enables us to categorize employees based on their energy-use patterns. Results demonstrate 6 distinct categories for occupants and entropy measures fully support the robustness of our model. Moreover, clustering along business days show a fairly uniform distribution of consumption patterns among all days of a week while each individual benefits from a specific subset of existing patterns. Obtained outcomes hold promise of efficient and inexpensive energy reduction by targeting and quantifying high and inconsistent energy consumers in the commercial sector. Holding targeted interventions and offering personalized feedback to building occupants will provide us with an enormous capability of energy reduction.

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2014 CRC PhD Student Poster Session

8. Adoption Readiness of Prevention through Design (PtD) Controls in Concrete, Masonry, and Asphalt Roofing Ari Goldberg (, Advisor: Dr. Deborah Young-Corbett Virginia Tech Concrete, masonry, and asphalt roofing operations are associated with some of the most pressing occupational health hazard risks in construction. The Prevention through Design (PtD) approach to controlling these risks involves the design of tools, equipment, systems, work processes, and facilities in order to reduce, or eliminate, hazards associated with work. Though PtD controls exist, the extent of their use is yet to be documented. To determine current usage trends and adoption-readiness of decision-makers regarding PtD controls in the concrete, masonry, and asphalt roofing trades. A survey instrument to capture information about current PtD control usage trends and decision-maker opinions about PtD controls was developed and validated. Controls investigated for concrete/masonry are dust collection equipment, wet-method systems, isolation systems, and sweeping compound. Controls investigated for asphalt roofing are asphalt tanker delivery systems, hot luggers/mechanical spreaders/felt-laying machines, insulated kettles/insulated hot luggers, low-fuming asphalt, and kettle fume guards. A telephone survey was completed of 365 decision makers in member firms of the Mason Contractors Association of America (MCAA)(n=700), the Concrete Sawing and Drilling Association (CSDA)(n=541), the American Concrete Pavement Association (ACPA)(n=4000), and the National Roofing Contractors Association (NRCA)(n=4000). Data analysis is currently underway. This poster will present the validated survey instrument and preliminary findings about PtD control solutions in these three construction trades. The results will enable and understanding of the barriers to adoption of healthier controls. The identification of barriers to adoption will allow for greater diffusion within the construction industry. The development of intervention strategies based on key constructs such as “perceived benefits”, “perceived risks”, “relative advantage”, and “trust in technology” is a novel contribution of the proposed work and offers the potential for translation of this work’s findings into a broader industrial population.

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2014 CRC PhD Student Poster Session

9. A Bio-inspired Virtual Pedagogical Environment to Stimulate Bio-inspired Thinking Aruna Muthumanickam (, Advisor: Dr. John E. Taylor Virginia Tech The BioBuild program: Building professionals are challenged with delivering complex built environments that meet the needs of shifting societal and environmental needs. Bio-inspired thinking is a comprehensive approach that can be used to address many of the problems of urbanization by applying answers from living systems, i.e., the biological world. The BioBuild program at Virginia Tech is a newly launched interdisciplinary effort that rigorously prepares professionals who can contribute effectively to the development of a bio-inspired built environment. CyberGRID - Virtual environment for pedagogy: CyberGRID (Cyber-enabled Global Research Infrastructure for Design) is a virtual environment created by researchers at Columbia University and Virginia Tech that provides an opportunity for geographically distributed teams to collaboratively work on design and construction projects. The use of the environment furthers civil engineering pedagogy by leveraging the benefits of simulating the professional project collaboration experience virtually. Bio-inspiration: In this poster, we describe the design of a bio-inspired version of the CyberGRID that draws inspiration from the vascular system of trees. The mechanism by which essential nutrients and water are transmitted to various plant parts inspires the translation of knowledge and dissemination into architectural elements. Bio-inspired CyberGRID: The bio-inspired version of the CyberGRID is a bio-mimicked virtual teaching-learning experience that focuses on preparing future professionals for the design and construction of bio-inspired built environments. This is done by providing an opportunity to experience bio-inspired structures (e.g., the Turning Torso in Sweden and the Gherkin in the UK) in a bio-inspired environment. Furthermore, the bio-inspired CyberGRID provides the infrastructure for student-led bioinspired projects to be developed, presented and stored for viewing by future course participants.

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2014 CRC PhD Student Poster Session

10. Optimizing the Sustainability of Single-Family Housing Units Aslihan Karatas (, Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign The sustainability of housing units can be improved by optimizing their social, environmental, and economic performances. The integration of green building equipment and systems such as geothermal heat pumps and water-efficient faucets often improves the social and environmental performances of housing units; however they can increase their initial cost and life cycle cost. Therefore, decision-makers need to carefully analyze and optimize the potential tradeoffs between the social, environmental, and economic performances of housing units. The main goal of this study is to develop novel multi-objective models for optimizing the sustainability of single-family housing units that represent 66% of the residential housing inventory in the US. To accomplish this goal, the research objectives of this study are to develop (1) an innovative housing social impact model that is capable of generating and analyzing optimal tradeoffs between the social quality of life for housing residents and the life cycle cost of housing; (2) a novel housing environmental performance model for maximizing the environmental performance of housing units while minimizing their initial cost; (3) a multi-objective optimization model that provides the capability of generating optimal tradeoffs among the three housing sustainability objectives of social quality-oflife, environmental performance, and life cycle cost; and (4) a scalable and expandable parallel computing framework that provides the capability of reducing the computational time of optimizing housing sustainability decisions and transforming this optimization problem from an intractable problem to a feasible and practical one. The performances of these developed models and framework were analyzed and refined using case studies of single-family housing units. The results of these performance evaluations illustrated that the developed optimization models were capable of generating a wide range of optimal solutions, where each identifies an optimal configuration of design and construction decisions that provides an optimal tradeoff among the three housing sustainability objectives. These novel research models and framework are expected to enhance the current practice of housing design and construction and contribute to maximizing the sustainability of single-family housing units.

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2014 CRC PhD Student Poster Session

11. Three-Tiered Data & Information Integration Framework for Highway Project DecisionMakings Asregedew Woldesenbet (, Advisor: Dr. “David� Hyung Seok Jeong Iowa State University State highway agencies invest a large amount of resources in collecting, storing and managing various types of data ranging from roadway inventory to pavement condition data during the life cycle of a highway infrastructure project. Despite this huge investment, the current level of data use for decision making is highly limited in many circumstances and is raising serious concerns whether the growing amount of data adds value to the final users and offers any meaningful return on the data collection efforts. To date, there has not been any standard procedure or a tool in the highway industry to integrate and assess the level of data utilization and/or help evaluate and justify these data collection efforts. This study presents a holistic approach that can systematically integrate and bridge data with information and decisions through incorporation of a unique and proactive performance assessment technique to improve the utilization of growing amount of data in transportation agencies for effective decision-making processes. With a focus on enhancing active utilization of data and measuring the level of data use in supporting highway management decisions, this approach delivers i) three-tiered hierarchical framework and ii) a new data and information performance assessment tool, Highway Infrastructure Data Integration (HIDI) index through the application of a social network theory. The HIDI index is developed to evaluate the status of data utilization that may serve as Highway Infrastructure Data Report Card and help justify the return on investment on the continuous and growing data collection efforts. It will allow agencies to interlink data, information and decisions and develop active utilization plan of currently existing databases to placing the right information in the hands of decision-makers. It will also allow agencies in the development of new data collection and knowledge generation plan to support key decisions which historically were not well-supported with information and data. This new framework may be used as a benchmarking example to State Highway Agencies in the area of data and information integration to make effective and reliable decisions through data-driven insights.

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2014 CRC PhD Student Poster Session

12. Minimizing effects of overfitting and collinearity in construction cost estimation: A new hybrid approach Bo Xiong (, Advisor: Dr. Martin Skitmore, Dr. Bo Xia Queensland University of Technology Overfitting and collinearity problems are commonly existed in current construction cost estimation applications and many other topics in construction management discipline. A hybrid approach of Akaike information criterion (AIC) stepwise regression and principal component regression (PCR) is proposed to solve overfitting and collinearity problems, and validated by comparing with other linear regression models. Mean square error by applying leave one out cross validation (MSELOOCV) is used in model selection during deciding predictive variables and principle components. The MSELOOCV of AIC regression model is 2.0% less than the default stepwise regression with minimizing the sum of squared error (SSE) and 19.9% less than the stepwise regression with maximizing adjusted R square and 48.2% less than the regression model with all predictive variables. The PCR is introduced after selection of predictive variables. In principal components selection, the SSE model and AIC model are selected for their MSELOOCV values much less than those of the adjusted R square model and the regression model keeping all principal components. The AIC-PCR approach not only solves overfitting and collinearity problems, but also improve the predictability with 7.8% less MSE than the default stepwise regression model. The validity of this approach is validated in this research and an AIC-PCR standard procedure is for researchers and practitioners to apply this approach in overcoming the widely existed overfitting and collinearity problems when dealing with other similar forecasting situations.

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2014 CRC PhD Student Poster Session

13. Automated Real-Time Tracking and 3D Visualization of Construction Equipment Operation Using Hybrid LiDAR System Chao Wang (, Advisor: Dr. Yong K. Cho Georgia Institute of Technology The interactions between workers, equipment, and materials can easily create visibility-related accidents. Visibility problems can lead to serious collisions without pro-active warnings. There have been a number of advances in vision-aid techniques because lacking full visibility is a major contributing factor in accidents at construction sites. However, unstructured work areas like construction sites are difficult to graphically visualize because they involve highly unpredictable activities and change rapidly. Construction site operations require real-time or near real-time information about the surrounding work environment, which further complicates graphical modeling and updating. One commonly used method to obtain the 3D position of an object is based on 3D laser scanning technology; this method, however, has some limitations, such as low data collection speed and low object recognition rates. It has always been a challenge to recognize specific objects from a 3D point cloud in unstructured construction environments because it is difficult to rapidly extract the target area from background scattered noises in a large and complex 3D point cloud. While rapid workspace modeling is essential to effectively control construction equipment, few approaches have been accepted by the construction industry due to the difficulty of addressing all the challenges of current construction material handling tasks with the current sensor technologies. The main objective of this research is to design, develop, and validate a 3D visualization framework to collect and process dynamic spatial information rapidly at a cluttered construction job site for safe and effective construction equipment operations. A custom-designed hybrid LiDAR system was developed in this study to rapidly recognize the selected target objects in a 3D space by dynamically separating target object’s point cloud data from a background scene for a quick computing process. A smart scanning method was also developed to only update the target object’s point cloud data while keeping the previously scanned static work environments. Then the target’s point cloud data were rapidly converted into a 3D surface model using the concave hull surface modeling algorithm after a process of data filtering and downsizing to increase the model accuracy and data processing speed. The generated surface model and the point cloud of static surroundings were wirelessly presented to the equipment operator. Validation of the proposed methodology was implemented at a real world construction jobsite. The 3D surface model of the tested equipment can be generated in less than a second, and then wirelessly transmitted to the operator in the cabin through a configured local network. The test results indicate that the proposed rapid workspace modeling approach can improve the heavy equipment operations by distinguishing surface-modeled dynamic target objects from the point cloud of existing static environment in 3D views in near real time. This research proposes fundamental research on 3D workspace modeling to foster a breakthrough innovation in robotic manufacturing and automated construction site operations which will greatly benefit the future U.S. construction industry and society in general. The knowledge obtained will enrich the literature in the important area of construction engineering and management and provide solutions to industry-driven problems. This research also proposes vigorous goals for integrating research and education.

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14. Interdependent Infrastructure Network System Vulnerability Identification Christopher Van Arsdale (, Advisor: Dr. Amlan Mukherjee Michigan Technological University For municipal infrastructure managers, identifying the vulnerabilities of infrastructure networks is a difficult task. As infrastructure networks become more interdependent, the ability to analyze and identify points that could disrupt network or system performance become more complicated while simultaneously becoming more important. By identifying the vulnerabilities in infrastructure systems, managers will be able to proactively plan contingencies and optimally utilize resources to avoid failures in vulnerable networks. In addition, Planners and designers will minimize the vulnerabilities in new or upgraded systems by having a specific measure to apply for certification from sustainability ratings systems such as EnvisionTM, e.g., criteria CR2.5 in EnvisionTM, requires “Documentation outlining potential traps and vulnerabilities and associated costs of risk�. The literature indicates that individual infrastructure network performance is based on the topology and flow in a network. However, these studies have not tried integrating multiple types of networks to assess their vulnerability from network co-dependence. When networks of infrastructure, traffic, or natural systems are interconnected, either physically or due to proximity, one network becoming unviable has the potential force other networks’ to become unviable. This research will help close this gap and provide decision makers metrics of their integrated infrastructure system vulnerabilities. Therefore, the objectives of this research are: 1) Develop metrics for robustness and resilience of an integrated infrastructure network. 2) Develop a method to identify critical points in an integrated infrastructure network consisting of natural and transportation networks and rank them in order of vulnerability. The fundamental hypothesis driving this research is that vulnerability of a system is a function of system resilience and robustness. Within the context of this research, robustness is defined as the amount of disruption a system can withstand before becoming unstable and unviable. Resilience is the time it a system takes to recover to a stable and viable state. Viability is defined as a state where the network or system meets the minimum level of service for its users. Methods based in Graph and network theory will be used. Viability theory will be employed to determine limits within which a system can viably deliver an acceptable level of service. A simulation system is being developed to test how alterations in the network topology and flow are likely to effect system performance. Specifically the study will test the sensitivity of networks to probabilistic link-disabling events. For example the loss of a road or bridge, or a rapid influx of traffic onto a highway effectively disables that link in the network and will affect the network performance. Current research is developing a framework to plot simulated measurements of system viability to identify states and conditions under which a network is stable and viable. The representation of the networks in question (transportation and natural networks for this research) will be constructed using data from existing GIS datasets.

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2014 CRC PhD Student Poster Session

15. Volatile Organic Compounds Emissions Generated in Hot-mix Asphalt Pavement Construction and Their Health Effects on Pavement Workers Dan Chong (, Advisor: Dr. Yuhong Wang The Hong Kong Polytechnic University Hot-mix asphalt (HMA) pavement is widely used in the building roads, airport runway, and parking lots. The unique characteristic of HMA pavement construction is that its placement and compaction have to be conducted at elevated temperature, which typically range from 135 °C to 150 °C. During this high temperature, massive amount of volatile organic compounds (VOCs) are generated and released to the air, which pose a potential health risk to pavement construction workers. The connection between the pavement workers’ exposure to VOCs and the pavement construction process has not been adequately addressed in existing studies. The aim of this study is to assess the VOCs generated in HMA pavement construction and investigate their potential effects on pavement workers’ health. Forty VOCs samples were collected from various locations (emission source points ESPs and worker breathing zone WBZs) and time points of six HMA pavement projects, and were subsequently analyzed in the laboratory for characterization of their chemical compositions and concentrations by using gas chromatography/mass selective detector (GC/MSD). The findings include the following: 1) the chemical species in VOCs at different time points and locations during HMA construction are closely correlated; 2) the VOCs concentrations during the pavement stage are higher than those during the compaction stages, and they generally decline with time; 3) porous asphalt mixture seemingly generates more VOCs emissions than the no-porous asphalt mixtures; 4) VOCs concentration is higher at the workplace in the absence of wind; 5) the majority of the identified chemicals are listed as hazardous materials by various occupational regulatory agencies; however, their concentrations at the ESPs and WBZs are below the mandated or recommended exposure limits, except for a few identified chemicals whose toxicological profiles have not been developed. Possible mitigation opportunities were also examined including emission source control, intervention in the VOCs propagation path, and receptor protection. This paper contributes to the knowledge of the types and concentrations of VOCs generated in asphalt pavement construction, their potential health risks to workers, and possible mitigation measures.

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16. Quantitative Performance Assessment of Single-Step and Two-Step Design-Build Procurement David Ramsey (, Advisor: Dr. Mounir El Asmar, Dr. G. Edward Gibson Arizona State University The use of design-build (DB) in the construction industry has become increasingly common, and several studies have shown increased project performance for DB as compared to other project delivery systems (e.g., Songer and Molenaar 1997, Konchar and Sanvido 1998, Wardani et. al. 2006). There are two primary methods used to procure DB services: single-step & two-step. Single-step DB involves design-builders responding to an owner’s solicitation that requires qualifications, technical, managerial and/or cost components in one submittal. Two-step DB involves first submitting a statement of qualifications and the owner shortlisting a limited number of firms, who are then invited to prepare full proposals. Major design and construction stakeholders feel that single-step DB places a lot of burden on the industry, mainly due to the risk associated with the extensive resources expended during project procurement. Quantitative performance assessments have not been completed to compare the effectiveness of single-step DB versus two-step DB procurement. The primary goal of this research is to examine the resource expenditure and efficiency impacts (e.g., the procurement performance) associated with single-step DB as compared to two-step DB. Three objectives were completed in order to achieve this research goal: (1) the resource expenditure from the industry perspective (e.g., the DB team) was calculated through studying procurement costs for each method. (2) Procurement schedule performance was quantified. (3) Quality and potential for innovation were also documented by collecting data about innovative solutions and various quality metrics for the procurement and project phases. In order to characterize the potential differences between single-step DB and two-step DB, the authors developed a research plan with three distinct phases. Phase I consisted of a thorough review of the DB procurement body of knowledge, as well as development of the data-collection survey with input from expert researchers and industry collaborators. Phase II consisted of data collection from project managers and project executives through surveys and follow-up phone interviews. Phase III consisted of univariate data analysis and interpretation of the results. Results show two-step DB is a more effective procurement method as compared to single-step DB. In fact, offerors of single-step DB projects are expending, on average, 5.43% of total project cost on the procurement phase. Conversely, offerors of two-step DB projects are expending on average 1% of total project cost on procurement. Additionally, there was no conclusive evidence showing the relative procurement schedule performance of single-step projects was reduced as compared to two-step projects. Moreover, performance-based technical requirements seem to be more prevalent in two-step projects over single-step projects, potentially leaving room for more innovation with two-step DB. Another interesting result is the two-step projects achieving a higher LEED rating than their single-step counterparts. Prior to this research, the literature was lacking a quantification of DB procurement costs based on actual project data. The contribution of this research is presenting clear performance differences by evaluating and quantifying key procurement metrics for single-step and two-step DB. The results are of extreme practical significance to the DB industry, in fact this study was funded by the Design-Build Institute of America (DBIA). The quantification of DB procurement cost and schedule can inform DB industry stakeholders about the potential implications associated with selecting single-step versus two-step DB procurement.

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2014 CRC PhD Student Poster Session

17. Risk Allocation in Public-Private Partnerships: Analysis of Contractual Provisions in 18 U.S. Highway Projects Duc A. Nguyen ( and Edwin Gonzalez (, Advisor: Dr. Michael J. Garvin Virginia Tech Public-Private Partnerships (PPPs) are typically characterized as: long-term contracts between the public and private sectors; where the private entity provides design, build, finance, operation, and maintenance services; and puts private equity at risk. Risk allocation is fundamental to PPPs since this delivery method is often justified based on risk transfer. The subject of risk has received significant treatment in the literature covering topics such as risk categorization, risk allocation, risk management methods, and risks and contracts. Yet, comprehensive studies of how risks are manifested and allocated within PPP project contractual provisions are generally absent. This investigation remedies this within the context of the United States by examining 18 PPP highway project contracts. The purpose of this research is to determine how the major risks in PPP projects are defined, allocated and managed within contracts. All U.S. highway PPP contracts, from 1993 to 2013, where the contract period exceeded 30 years and private equity was at risk are examined. Project risks were categorized and identified based on a comprehensive literature review. Subsequently, a rubric for risk identification, characterization, and allocation within PPP contracts was constructed and tested; the rubric is being applied to the 18 highway projects. Using content and comparative analysis techniques, we are analyzing the outcomes of the rubric’s application to the project set to contrast provision language and risk allocation by various project characteristics such as payment mechanism, jurisdiction, and project scope. Results provide a comprehensive overview of contractual risk allocation in U.S. PPP highway projects. We observe that risk allocation and management provisions have changed over time and are similar along some dimensions while varying along others. For example, risk allocation appears to be influenced by different jurisdictions: while latent defects risk in the Presidio Parkway project (California, 2012) is held solely by the public authority, that same risk is shared using a deductible scheme between the public authority and the private developer in the I-595 project (Florida, 2009) although both use availability payments. Contrasting two toll projects–the I-495 project (Virginia, 2007) and the I-635 project (Texas, 2010)–indicates interesting differences as well. In I-635, the public authority bears site acquisition risk while shares network, material adverse, and latent defects risks with the private developer. Yet in I-495, the private developer solely bears all of these risks. The Presidio Parkway is one of the most recent projects, and its contract introduces an innovative flexibility provision by giving the private developer an option to choose either availability payments or tolling mechanisms. The research is significant since it is one of the first systematic and comprehensive examinations of PPP contractual provisions done. It has also demonstrated the importance of jurisdictional preferences and market precedence on PPP contracts. It will also provide a baseline understanding of risk allocation in U.S. highway PPPs. Additional investigation of the relationships among the risks, socioeconomic factors and project characteristics can reveal patterns for successful risk allocation in PPPs. Further, the baseline developed and the comparisons drawn have important practical value since practitioners may examine how contractual provisions and risk allocation have evolved and been implemented in various jurisdictions and types of projects.

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18. Extending Building Information Modeling (BIM) Interoperability to Geo-Spatial Domain using Semantic Web Technology Ebrahim P. Karan (, Advisor: Javier Irizarry Georgia Institute of Technology The decision making process in a construction project is based on available information (usually extracted from different sources) coupled with the domain knowledge possessed by an individual. Each representation of an object or input in the individual’s mind is tagged with a meaning. When making a decision, it is often not enough to merely access information; rather, it is necessary to understand the meaning (or semantic) of the acquired information. Thus, the semantic web technology is used in this study to discover the relationships between these different sets of semantics and depict whether the pair of concepts are similar or not. Currently, the results of Semantic Web queries are not supported by any building information modeling (BIM) authoring tools. The purpose of this study is to extend the interoperability of BIM authoring tools in construction domain by employing semantic web technology. This research develops an ontology based on Industry Foundation Classes (IFC) schema to publish BIM data as the semantic web data format and also provides a query rewriting method to translate query results into the XML representations of the IFC schema and data. Using the concepts and relationships in an IFC schema, we first develop ontology for construction operations to translate building's elements into a semantic web data format. Then, a mapping structure is defined and used to integrate and query the heterogeneous spatial and temporal data. Finally, we use a query language to access and acquire the data in semantic web format and convert them into the XML representations of the IFC schema, ifcXML. Through two scenario examples, the potential usefulness of the proposed methodology is validated. Also, the resulted ifcXML document is validated with an XML schema validating parser and then loaded into a BIM authoring tool. The research findings indicate that the format of the output is the most important component of the query results. The developed interface for representing IFC-compatible outputs allows BIM users to query and access building data at any time over the web from data providers. Linked data, as a concept that arises within the paradigm of semantic web, helps to overcome interoperability challenges to enhance information exchange in the construction domain. Also, it is concluded that Semantic web technology can be used to convey meaning, which is interpretable by both construction project participants as well as BIM applications processing the transferred data. The models developed for extending interoperability between BIM and geo-spatial analysis tools are focused at the syntactic level, in which the emphasis is on integrating two or more data models into a single, unified, model. A higher level of integration (i.e. semantic interoperability) is used at this study to share information and their meanings between BIM and Geo-Spatial datasets. Currently, the results of Semantic Web queries are not supported by BIM authoring tools. Thus, the proposed methodology utilizes the capabilities of ontology languages to transform the query results to an XML representation of IFC data.

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19. Quantifying the risks of wildfire to buildings in Wildland Urban Interface: a forward view Elmira Kalhor (, Advisor: Dr. Vanessa Valentin University of New Mexico Wildfire is a complex phenomenon that reforms ecosystems, changes species habitat and changes our lifestyle. The cause and spread of wildfire, has social, ecological, spatial, atmospheric and economical factors all of a dynamic nature, which need to be considered when evaluating wildfire risk. Wildfire research has spread across disciplines of science and research from ecology to sociology to economics to management and engineering. Hence, the risk of wildfire has different meanings for different researchers. Despite its significant role, however, wildfire research is limited in the field of urban planning. Wildfires often find their way to the Wildland-Urban Interface (WUI) where residential buildings get closer to the wildland (national forests, national parks, etc.). Regardless the increase in frequency and severity of wildfires, housing development projects progress further towards these flame zones increasing the vulnerability of the community. The long term goal of this study is to answer the question: how urban planning and safety management can account for the risk of wildfire to buildings, specifically those located in WUIs? The objective of the first component of this research is to find a distinct probability function associated with any location of interest (present or potential buildings) which explains the likelihood of having a fire of a precise intensity (heat and flame length) at a specific location. Fire intensities will be translated into building damage, in order to find the total risk of wildfire as the intersection of probability and damage. In order to define the aforementioned probability functions, this research expands on existing advanced fire propagation models. These models will be coded and run for a variety of scenarios to find the frequency of occurrence of specific fire intensity for urban buildings. Significant factors affecting the probability of wildfire occurring on a specific location will be identified. The results of this study provide reliable probability functions for defining the likelihood that a specific building in the WUI will be affected by a potential fire and the expected impact of this event. The risk model is used to minimize the risk of fire by producing optimum land use designations. Few models in the management discipline have used regression analysis to find the risk of wildfire considering different study zones in order to prioritize the mitigation measures of federal land agencies. The novelty of this study comes from the focus on probability for calculating wildfire risk in urban planning applications. All possible fire events are simulated using available advanced thermodynamic formulations. The results of this research appear in the form of a Decision Support System (DSS) that not only is usable for urban planners but is also helpful for the insurance industry and federal land agencies. This DSS helps managers to optimize urban design, to allocate emergency resources, and to prioritize preventive actions.

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20. Collaboration through Innovation: A Multi-Layered Framework for the AECM Industry Erik A. Poirier (, Advisor: Dr. Daniel Forgues, Dr. Sheryl Staub-French École de Technologie Supérieure The contemporary landscape of project delivery in the Architecture, Engineering, Construction and Maintenance (AECM) industry is being redesigned through two innovations: Building Information Modeling (BIM) and Integrated Design and Project Delivery (IDPD). Both these innovations epitomize the radical shift towards integration of practice, of process and of information. Consequently, they are pushing us to rethink how and why we collaborate. Considerable research work has aimed at developing tools and processes that foster collaboration by focusing on these particular innovations. In this light, collaboration is typically viewed as a means to an end; its measures of improvement and success are generally limited to project outcome. However, even through progress, collaboration remains amorphous. As a core tenet of the AECM industry, collaboration should be examined beyond this teleological stance. From this perspective, considerable gaps appear around the emergence and evolution of collaboration through innovation. The principle research objective of this project is to investigate the dynamics that characterize the emergence of collaboration through innovation. The research project aims to address the gaps that appear when attempting to assess what is structuring, motivating, mediating and informing collaboration from an evolutionary perspective. The focus is on integration of information, of practice and of process through innovation, in particular BIM and IDPD. Secondary objectives mirror an iterative and grounded approach to research: In a first cycle, a framework is built; the aim is to characterize collaboration in the AECM industry. In a second cycle, the framework is operationalized and tested: the aim is to operationalize the framework and assess the impact of these innovations on collaboration, its emergence and its evolution. This research project adopts a constructivist approach to grounded theory; it is rooted in the interpretivist paradigm. The methodology is case study based, which allows an in-depth enquiry of phenomena through a mixed-method approach to data: both qualitative and quantitative data are being collected and analyzed. Five case studies have been targeted representing the full project continuum. The intent is to maximize the scope of theoretical sampling. The constructive grounded theoretical approach underlies an iterative process through which theory emerges and can be tested in an attempt to reach theoretical saturation. Thus, the case studies are serving as the basis to build, operationalize, test, validate and saturate the framework. The results of this research project are articulated around two threads: developing the framework and operationalizing it. To date, the framework has developed into three embedded layers, which have emerged from the data - the agentic layer, which informs the structural layer, which in turn mediates the operational layer . The emergence of collaboration and its evolution (as opposed to its finality) are at the core of the framework. When operationalized, the framework intimates alignments amongst layers as a way to develop collaboration. Preliminary findings suggest that alignments within and across these layers between individual project team members tend to enhance collaboration. In this regard, we have observed a positive emergence of collaborative behaviours and actions through integrative innovations that were implemented within an ‘aligned’ project setting. On the other hand, misalignments have been observed to contribute to ‘misfires of innovation’, a misuse or underdevelopment of said innovations. In this regard, we have observed a hindrance to collaborative behaviours and actions through integrative innovations that were implemented within a ‘misaligned’ project setting. The anticipated scientific contribution of this research project is the development of a substantive theoretical framework aimed at characterizing the emergence and evolution of collaboration through innovative approaches to project delivery in the AECM industry. Where most research in this field adopts a positivist stance aimed at developing tools to improve collaboration, this research project takes a constructivist stance to characterize layered categories of collaboration and study how innovations impact their emergence and evolution. It offers an alternative perspective while highlighting areas for future enquiry across these categories. In terms of practical significance, this substantive theoretical framework is grounded in practice. It is scalable and aggregatory, i.e. it can be applied across scales and scopes. As such, its application offers a common language to foster collaboration in practice and can serve in orienting focus when implementing innovations, which span project networks.

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21. The Virtual Construction Simulator: An Educational Game in Construction Engineering Fadi Castronovo (, Advisor: Dr. John I. Messner The Pennsylvania State University The construction of a facility is a dynamic process, governed by complicated problems and solutions. This complex nature poses instructors the difficult task of developing pedagogical strategies to teach engineering students how to tackle such processes. However, traditional construction planning and management teaching methods have been criticized for presenting students with well-defined problems, which don’t reflect the challenges present in the industry. An innovative teaching method – educational simulation games – has shown potential in teaching students complex construction processes, in a simulated construction environment. In addressing these instructional challenges, we will illustrate the lessons learned in the development and design of complex serious games. The work presented is a result of the current research efforts in the development of the Virtual Construction Simulator (VCS) a project supported by the National Science Foundation.

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2014 CRC PhD Student Poster Session

22. Predictive Emissions Models for Excavators Heni Fitriani (, Advisor: Dr. Phil Lewis Oklahoma State University Heavy duty-diesel (HDD) construction equipment consumes large quantities of fuel and subsequently emits significant quantities of air pollutants. In most of construction activities, HDD construction equipment is the primary source of emissions. The purpose of this poster is to demonstrate two different predictive modeling methodologies for estimating emission rates for HDD construction equipment particularly excavators. The model were developed using real-world data collected using Portable Emissions Measurement System (PEMS). The modeling methodologies used are Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). These modeling techniques were used to produce models to predict emission rates of nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). Results show that in most cases, the MLR approach produced highly precise models for NOx, CO2, and PM. The models for HC and CO were less precise with R2 values ranging from 0.09 – 0.80. However, ANN models performed the best with regard to precision, accuracy, and bias. Overall, the results of this study help quantify and characterize the air pollution problems from HDD equipment used in construction. The methodologies presented may certainly be used to develop emissions models for other types of equipment.

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23. Estimating Extreme Event Recovery with Construction Activity Change Points Henry D. Lester (, Advisor: Dr. Gary P. Moynihan University of Alabama Repairing and rebuilding structures following an extreme event requires a substantial outlay of resources to achieve full disaster recovery. Demographic shifts toward highrisk communities are increasingly placing both populations and the built environment at substantial loss exposure to such extreme events. Instead of retaining extreme event risks associated with these communities, property owners expect to transfer the risk to government regardless of the exploding municipal debt. The shrinking municipal assets restrict availability of resources for extreme event recovery operations. The associated construction increases in disasterprone areas dictate disaster planning to safeguard at risk populations during the recovery phase of the disaster life cycle and this planning necessitates a temporal recovery metric. This poster presents research employing a change point approach to estimating extreme event built environment system recovery. Specifically, the research considers Fisher type price adjusted spatial new singlefamily residential building permits as a time series signifying residential construction activity. The research compares changes in this residential construction activity with accompanying declared disasters to ascertain any relationships. The approach examines spatiotemporal residential construction variability to determine built environment rapidity by measuring the duration between sequential time series change points. The change point approach and resultant temporal recovery metric allows estimation of extreme event built environment system recovery for decisions makers to conduct operative disaster planning and municipal resource allocation.

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24. An integrated simulation and optimization based residential construction carbon footprint and emission assessment Hong Xian Li (, Advisor: Dr. Mohamed Al-Hussein, Dr. Mustafa Gßl University of Alberta Winter heating on residential construction sites consumes a considerable amount of energy and emits a significant volume of greenhouse gases (GHGs) in cold regions—space heaters run continually in winter months of construction. Taking panelized residential house construction for instance, it has been reported that on-site winter heating accounts for as much as 34% of carbon emissions from the framing phase. The objective of this research includes the following: (1) Quantify carbon emissions from on-site construction, including emissions from the construction process and winter heating. A combined discrete-event simulation (DES) and continuous simulation is employed as the quantification approach, based on panelized construction as the construction technology. (2) Propose plans to reduce carbon emissions from on-site construction in cold regions. Based on the simulation model, crew sizes for labour-intensive activities are analyzed and optimized with respect to carbon emissions from construction in cold regions. A discrete-event simulation (DES) platform, Simphony.NET, is employed to simulate the on-site construction process, while the impact of weather on winter heating is considered using the continuous simulation template recently developed at the University of Alberta. Based on the construction process simulation, a genetic algorithm (GA) is employed as the optimization approach, i.e., integrating the simulation and optimization models. The integrated simulation and optimization methodology is illustrated using a case example in Edmonton, Alberta, Canada, with on-site construction activities commencing at intervals corresponding to the four seasons considered and compared. The results demonstrate that: (1) the combination of DES and continuous simulation is an appropriate approach, capable of automatically simulating the construction process as well as winter heating; (2) by optimizing on-site crew size, the carbon emissions of construction are reduced significantly (a margin of 4.29 kg CO2/m2 is identified in this research); (3) the effect of optimization depends on the construction start date. This research thus proposes a generic methodology by which to measure and minimize the carbon emissions from on-site construction in cold regions. This research proposes a generic methodology of carbon footprint and emissions assessment with integrated simulation and optimization for residential construction.

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2014 CRC PhD Student Poster Session

25. 3D Reconstruction of Industrial Equipment Using Combined Geometric and Topological Information from Laser-Scanned Data Hyojoo Son (, Advisor: Dr. Changwan Kim Chung-Ang University Industrial equipment plays a vital role in operative procedures as a basic part of industrial facilities. In the operation and maintenance phase of these facilities, it is important to ensure that the as-built condition of each installed piece of equipment and any changes that may subsequently occur are properly recorded and adjusted for the reference of the engineers and managers in the interpretation process. Recently, 3D measurements of industrial facilities’ as-built conditions have been done efficiently by using terrestrial laser scanners. The resulting detailed and often complex set of 3D point clouds is then processed to manually create digitalized as-built models of the industrial equipment. However, the manual creation of industrial equipment models from an acquired set of 3D point clouds is difficult. This paper proposes a framework for an automated approach to the reconstruction of 3D models of industrial equipment from terrestrial laser-scanned data. The proposed approach consists of four main steps: the extraction of sets of 3D point clouds that constitute equipment and adjacent pipelines; the representation of geometric and topological information from the extracted 3D point cloud sets, the existing piping and instrumentation diagrams (P&ID), and the 3D database; matching the representations from the P&ID and the 3D database to the representations from the 3D point cloud sets; and the registration of the selected model to the 3D point cloud set. Experimental evaluation demonstrates that the proposed approach overcomes the major limitations that arise when industrial equipment is treated as primitives and when only geometric characteristics are considered in the matching and retrieving step. The proposed approach could be successfully utilized to reconstruct asbuilt industrial equipment during the operation and maintenance of industrial facilities. During this process, furthermore, all object models were tagged with their information predefined in the P&ID. Adding such information to 3D models is beneficial for many analytical and managerial purposes. Future work will be devoted to experimentation on domains with more complex scenes, as well as a wider range of processes for practical use purposes.

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2014 CRC PhD Student Poster Session

26. Information Extraction and Automated Reasoning for Automated Regulatory Compliance Checking in the Construction Domain Jiansong Zhang (, Advisor: Dr. Nora El-Gohary University of Illinois at Urbana-Champaign Manual checking of construction regulatory compliance is time-consuming, costly, and error-prone. Automating the process of compliance checking is expected to reduce the time and cost of the process, as well as reduce the probability of making compliance assessment errors. Previous research and development efforts in automated compliance checking (ACC) have paved the way, but the current extraction and encoding of regulatory and project information into computer-processable format still require much manual effort and there lacks the level of knowledge representation and reasoning that is needed for compliance analysis and checking. Develop methodologies/algorithms and corresponding knowledge representations that automatically extract regulatory and project information from textual regulatory documents and BIM models, respectively, encode the information in a semantic format, and automatically reason about the information for compliance assessment. Utilize a theoretically-based, empirically-driven methodology that leverages knowledge and techniques in natural language processing (NLP), semantic modeling and automated reasoning to develop methodologies/algorithms and corresponding knowledge representations for automated information extraction (IE), automated information transformation (ITr), and automated compliance reasoning (CR) for regulatory information and project information in the construction domain. Preliminary Results: a) Hybrid syntactic-semantic rule-based methodology and algorithms for automated IE from construction regulatory documents; b) Semantic rule-based methodology and algorithms for ITr; c) Logic-based methodology and algorithms for CR. Scientific Contributions and Practical Significance:  Offer efficient-to-develop, semantic rule-based NLP methods for IE and ITr that can help capture domain-specific meaning;  Combine domain knowledge and NLP knowledge to achieve deep NLP;  Pioneer work in utilizing semantically deep NLP approach in the construction domain;  Provide benchmark semantic methods/algorithms for IE and ITr in the construction domain;  Offer a new mechanism (“consume and generate” mechanism) for processing and transforming complex regulatory text into logic clauses;  Pioneer work in utilizing logic programming (LP) for the automated reasoning functionality in ACC in the construction domain;  Reduce the time and cost of compliance checking in the construction domain;  Improve the accuracy of compliance checking;  Support other applications of automated information processing in the construction domain.

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27. Ex-ante Assessment of Performance in Construction Projects: A System-of-Systems Approach Jin Zhu (, Advisor: Dr. Ali Mostafavi Florida International University Early and accurate assessment of performance is critical in successful delivery of complex construction projects. Construction projects are complex Systems-of-Systems (SoS) consisting of different interconnected networks of processes, activities, human agents, resources, and information. The interconnections between different constituents in construction projects give rise to project-level emergent properties that affect the ability of project organizations to cope with uncertainties, and thus, influence project performance. Hence, better assessment of performance in construction projects requires integrated analysis of the existing emergent properties, dynamic behaviors, complexities, and uncertainties. The overarching objectives of this research were to: (1) create and test an integrated framework for bottom-up assessment of performance in construction projects using a SoS approach, and (2) explore the emergent properties affecting the ability of project organizations to cope with uncertainties. The study created and tested an integrated framework using a SoS approach in which construction projects were evaluated across four levels of analysis: base, activity, process, and project levels. The outcomes of each level of analysis were obtained by aggregating the dynamic interactions at the levels below. The abstraction and simulation of the dynamics of construction projects were made at the base level, where the interconnections between the dynamic behaviors of the players, information requirements, and resource utilization were investigated. The application of the framework was demonstrated in a case study related to a tunneling project. A hybrid agent-based/system dynamics model was created and validated and was then used in conducting Monte-Carlo experimentations to investigate the impacts of the micro-level behaviors on the macro-level performance patterns. The results highlight the significance of incorporating the dynamic behaviors of human agents as well as information processing in modeling performance in construction projects. In addition, the results reveal the highly likely scenarios for enhancing the ability of project organizations to cope with uncertainties through streamlining information processing and modifying dynamic behaviors of human agents. The results also show that the ability of project organizations to cope with uncertainties could be captured based on three emergent properties at the project level: absorptive capacity, adaptive capacity, and restorative capacity. These emergent properties could be used as leading indicators for ex-ante evaluation of project success. This distinctive approach is the first of its kind to simulate the performance measures at the project level based on the micro-behaviors of project constituents at the base level. The proposed framework provides an integrated approach for investigating the performance of construction projects at the interface between the dynamic behaviors of players, uncertainties, and complexities. Hence, it could provide a robust basis towards creation of integrated theories of performance assessment in construction projects. From a practical perspective, the framework and emergent properties could be used in developing leading indicators for predictive assessment and proactive monitoring of performance in construction projects.

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28. A Framework for Public Private Partnership Risk Mitigation in Rural Post Conflict Environments– A Systems Approach John T. Mitchell (, Advisor: Dr. Yvan Beliveau Virginia Tech Post conflict situations regardless of locale have a sense of fragility. High social and political tension combined with devastated infrastructure make hopes for redevelopment tenuous. Urban areas are usually the point of focus for redevelopment action as their populations are typically inflated from the conflict. Ordinary rural lifestyles are forced to cope with now crowded conditions in locations where original infrastructure provisions were possibly stressed, and damaged ones much less suitable. This doctoral research focuses on the development, design, and implementation framework to reduce risk and increase participation in Public Private Partnerships to deliver key rural infrastructure and generate employment and business opportunities. The research methodology is based on a multisector integrated systems design framework that is comprised of: 1) sustainable energy system component; 2) education component; and 3) a local existing economic generator. The introduction of off-grid sustainable energy in combination with targeted education could stimulate growth of an existing economic enterprise with viable jobs and business opportunities. Non- conflict zone governments in resource rich locations of Africa are also challenged to provide revenues to rural infrastructure developments much less those of post- conflict situations. Public Private Partnership (PPP) is an ideal delivery mechanism for such investment because of its long term contract requirement. Based on relational contracting philosophy PPP could optimize the utilization of all stakeholders’ knowledge for infrastructure provision and efficient and effective long term operation and income generation. In many instances the failure of PPP is the result of under-utilization of facility output and or inability to pay for service. The delicate nature of Post conflict Governments exacerbates the situation and reduces funding access. It is with this understanding that the framework proposes infrastructure implementation that will generate alternative business development and local job creation as a priority thereby facilitating the ability to pay for service. The creation of additional business opportunities within the construct of the PPP allows the investors to spread their investment risk across other income generating areas. This formula is a typical investment risk mitigation strategy and in this case depending upon the viable enterprises identified has the potential of increasing the investment participant pool and knowledge availability, and maximizing market and customer access. The country of Somalia is used as a base case for testing the framework. Crops, forestry, and forage have been examined to identify constraints to livestock and farm productivity, impacts on food security and opportunities for local economic development. A systems approach links power provision with the required educational training and food processing systems to provide and maintain a sterile food processing environment. This framework is to be reviewed by two separate panels of experts in international development and infrastructure via questionnaire and semi-structured interviews from which the preliminary results will facilitate the refinement of the framework. The linking of biomass production and rural off-grid electrification models in Somalia is the first attempt to enable local communities to improve environment and ecology, increase farm and livestock productivity and produce adequate power to ensure safe food production and storage. The research identifies possible approaches to biomass based electrical output utilizing micro turbines in combination with gasifiers or digesters for direct electricity production, and the use of auxiliary heat in the combined heat and power (CHP) cycle to purify and supply high temperature water to reduce food borne pathogens so that safe food supplies can be ensured. The energy technology in combination with required educational training will also enable local communities to safely process and store food products that meet standards of export markets and thereby increase their business and job creation potential. Page 36 of 84

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29. Framework for On-Site Biomechanical Analysis During Construction Tasks JoonOh Seo (, Advisor: Dr. SangHyun Lee University of Michigan In the labor-intensive construction industry, workers are frequently exposed to manual handling tasks involving forceful exertions and awkward postures. As a result, construction workers are at about a 50 percent higher risk of work-related musculoskeletal disorders (WMSDs) than workers in other industries. One of the methods for assessing workers’ exposure to the risk factors of WMSDs is a biomechanical analysis that estimates musculoskeletal stresses (e.g., corresponding joint moments and muscle forces) as a function of human motion (e.g., postures and movements) and external forces (i.e., exerted forces on hands and feet). Biomechanical analysis enables us to identify hazardous tasks by comparing musculoskeletal stresses with human physical capability (i.e., strength). In previous studies, however, the motions and forces required for biomechanical analysis have been measured using complex motion capture (e.g., marker-based motion capture systems) and force measurement systems (e.g., force transducers) in controlled environments. Therefore, it is difficult to consider possible variations of construction tasks that exist under real conditions. To address this issue, we propose a framework for on-site biomechanical analysis for construction manual tasks by integrating markerless vision-based motion capture approaches and motion-driven force estimation that enable us to collect motion and force data required for biomechanical analysis under real conditions. First, the proposed framework extracts skeleton-based motion data by tracking body parts and joints in 2D images from ordinary video cameras, or in 3D images from an RGB-D sensor. Next, data about the forces exerted on hands and feet to perform certain tasks is predicted according to types of tasks. For example, for lifting tasks, hand loads and foot forces can be estimated based on the weight of an object lifted and the body weight. For more complex tasks, such as ladder climbing—in which external forces are dynamically changing—we propose force prediction models. For example, patterns of external forces during ladder climbing have a significant relationship with individual (e.g., workers’ climbing styles) and physical (e.g., ladder slant) factors, and thus the external forces can be estimated by identifying their quantitative relationships and by modeling force patterns under dominant factors. Finally, the proposed framework performs a biomechanical analysis using motion and force data from previous steps to identify excessive musculoskeletal stresses beyond human capability. For the biomechanical analysis, computerized biomechanical analysis tools such as 3DSSPPTM and OpenSim are used. As motion data from markerless vision-based approaches is not directly available to existing tools, we also propose automated processes to convert the motion data into available data in biomechanical analysis tools. To test the feasibility of the proposed framework, we conducted case studies on the masonry work and on a ladder climbing activity. The results from the case studies showed that the proposed framework can be successfully used to perform biomechanical analyses on diverse tasks by showing similar results from previous experimental biomechanical studies. The proposed framework has great potential to broaden our understanding of the causes of WMSDs by estimating musculoskeletal stresses on the human body during construction manual tasks without any invasive measures. Thus, it can identify potentially hazardous construction tasks, contributing to minimizing the risks of WMSDs by providing behavioral feedback to workers or by redesigning work processes and environments that may cause excessive musculoskeletal stresses.

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30. Develop a Price Escalation Method for Single Award Indefinite Delivery/Indefinite Quantity Contracts: AxE Bidding Jorge A. Rueda (, Advisor: Dr. Douglas D. Gransberg Iowa State University As a result of a comprehensive research conducted for the Minnesota Department of Transportation (MnDOT) on the current Indefinite Delivery/Indefinite Quantity (IDIQ) practices adopted by different transportation agencies across the US, the research team has identified a major issue to be addressed before MnDOT can fully implement IDIQ contracting: cost escalation in multi-year single award IDIQ contracts. This study introduces a new escalation methodology and terms it: Cost times Escalation (AxE) bidding. It seeks to eliminate the need to depend on external construction cost indices or to develop a MnDOT construction cost index by shifting the escalation risk to the contractor during bidding and allowing it to propose its own escalation adjustment factor. The proposed process requires competing contractors to submit a fixed annual adjustment rate, which will be used to modify bid unit prices over time throughout the IDIQ contract’s life cycle. The adjustment rate is also factored into the selection of the low bid in a manner similar to A+B bidding. This study evaluates different alternatives to incorporate this rate into the selection of the successful contractor (formulas for E) and quantifies the risk related to each alternative for different case scenarios. Additionally, AxE bidding is expected to reduce construction costs and agency staffing requirements, as well as overcome some disadvantages associated with using traditional price escalation methods, such as the lack of flexibility to adapt to the nature of the contract and the inability to consider imminent future changes in the construction industry. This study also presents an analysis of traditional price escalation methods by applying twelve different cost escalation indexes, and one alternative method currently used by MnDOT on its IDIQ contracts, on four case study projects over a five-year period. Outcomes from each index were compared with observed bid prices along the same period of time. A complete analysis of these traditional price escalation methods and historical bid data were used by the authors as a reference to develop an escalation method that meets MnDOT needs.

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31. Construction Operations Automation using Modified Discrete Event Simulation Models Joseph Louis (, Advisor: Dr. Phillip S. Dunston Purdue University Although the automation of construction tasks has been an active research topic since the 1970s, few actual robots can be found on the worksite today and the fundamental construction process has remained unchanged since the pre-industrial era. On the other hand, the planning and design stages of construction projects has benefited greatly from the advances made in simulation and modeling technologies for construction operations and products. In this research, a novel mechanism for leveraging information-rich simulation models to automate construction operations is developed. The methodology presented uses Discrete Event Simulation (DES) models of operations to drive the process in the real world using autonomous robots. The following specific research questions will be addressed in this research to enable the automation of operations: (1) What modifications are required to enable DES models, traditionally used only to analyze operations, to serve as control mechanisms to orchestrate autonomous equipment and thus enable operation automation? (2) How can the DES models, modified for operations control, be verified and validated before their use in the real world? (3) How can the modified DES models be used to automate any construction operation, regardless of scale and complexity? Technically, the need for a rethinking of traditional DES models is necessitated by the fact that the durations of activities will not be known a-priori when the model is controlling a real world operation in real time. The state of the art in construction simulation, visualization, and automation serves as the foundation to answer the questions that pave the path towards the overarching goal of construction automation. An initial demonstration of the proposed framework’s feasibility was performed using modified RC models of construction equipment. A discussion of the results and conclusions of this preliminary experiment are provided in the poster. The primary contribution of the proposed research is the use of DES models in an unprecedented manner to enable the construction automation. This approach would allow for the automation of any construction operation, given the application breadth of DES models that allow it to faithfully represent almost any construction process. Another significant advancement that the proposed methodology enables is the possibility of remote construction in extraterrestrial, underwater, and other hazardous environments, which is currently impossible without the presence of human operatives. The proposed methodology, while presented in the construction context, has important implications for the automation of any industry characterized by a less-structured and high-uncertainty environment, wherein tasks are subject to complex interaction between disparate resources, such as agriculture. Apart from the benefits of automation described above, the proposed research has immediate applicability for the monitoring and control of conventional construction sites, i.e. without the presence of automated equipment. This research also enables new visualization techniques at the operation/ process level in field construction with the use of robot simulators. The use of reusable CAD models in robot simulators encapsulated with their performance data and functionality would free up the operation modeler from low level details about equipment and from the collection of activity duration data and geometric data about the work site.

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2014 CRC PhD Student Poster Session

32. Autonomous Near-Miss Fall Accident Detection Technique Using Inertial Measurement Units on Construction Iron-Workers Kanghyeok Yang ( and Sepideh S. Aria (, Advisor: Dr. Changbum Ahn University of Nebraska at Lincoln In the construction industry, fall accidents are one of the leading causes of fatal and non-fatal occupational injuries. Previous research has focused on the qualitative assessment of fall accident risk or on the investigation of a lagging indicator to acquire a better understanding of construction fall accidents. However, an investigation of potential fall accidents is still challenging due to the scarcity of relevant data and of appropriate investigation approaches. These circumstances limit proactive fall accident prevention efforts. One promising technique for investigating possible accidents is the implementation of a near-miss accident reporting system. However, previous near-miss reporting systems are based upon workers’ retrospective and qualitative self-reporting rather than autonomously measured quantitative data. The objective of this research is to introduce an autonomous near-miss fall accident detection technique that employs wearable Inertial Measurement Units (IMU) to document the stability (i.e., degree of loosing balance) of construction iron-workers. Considering iron-workers’ diverse postures and positions on steel beams during their work, the first step in detecting near-miss fall accidents in an actual worksite requires being able to accurately classify postures and movements to determine workers’ stability conditions. Towards this end, this research examined the near-miss fall detection technique for workers’ walking activity. In this research, two experiments were conducted using video data and a worker’s IMU data—in order to acquire worker’s body-movement and stability data, an IMU was applied to the iron-worker’s sacrum to record movements and postures. In the initial laboratory experiment, two different activities (i.e., diverse postures and walking) were performed on a rectangular steel I-beam frame (12’ x 6’) for posture classification and near-miss fall detection. Subsequently, a second experiment translated only the near-miss fall detection methodology into a site-level experiment to verify the technique’s potential for implementation in an actual construction site. In the data processing stage, this research extracted 38 features (i.e., Mean, Standard Deviation, Max, Correlation, Spectral Entropy, and Spectral Centroid) from the raw IMU data. Based upon these features, this research implemented machine learning techniques (i.e., a Support Vector Machine and Laplacian-Support Vector Machine) for posture/movement classification and near-miss accident detection. Through the experiments and the machine learning techniques, five different worker’s postures could be classified. In the laboratory experiment, posture classification using the support vector machine had over 95% overall accuracy. For near-miss fall detection the Laplacian support vector machine managed 98% accuracy in detecting near-miss accidents during walking motions. As would be expected considering worker’s expertise, only a small number of near-miss accidents could be placed during the site experiment. Through the same near-miss fall detection technique, a 99% accuracy was achieved in the site-level experiment data. The near-miss fall accident detection technique introduced in this research will help to identify potential fall accidents at an individual level and inform those at risk. By aggregating individual data, this technique could also facilitate the locating of dangerous spots in a worksite, thereby empowering workers to install additional fall protection measures. Moreover, autonomous detection of near-miss accidents will enhance the overall understanding of fall accident in construction.

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2014 CRC PhD Student Poster Session

33. Managing Water and Wastewater Infrastructure in Shrinking Cities Kasey Faust (, Advisor: Dr. Dulcy Abraham Purdue University The research presented in this poster highlights select water and wastewater infrastructure management challenges in shrinking cities, proposes management options to address these challenges, and evaluates the feasibility of a decommissioning, one of the management options. Major economic downturns in once vibrant industrial cities have resulted in the loss of significant populations and tax bases. This phenomenon, termed as shrinking cities, introduces enormous challenges to managing major infrastructure systems. When cities experience catastrophic economic conditions, consequentially causing extreme population loss, maintaining critical infrastructure at original levels of operation becomes unsustainable. While this study focuses on water and wastewater infrastructure, other forms of infrastructure (e.g., power and gas utilities) are equally susceptible. A key challenge affecting shrinking cities is the fixed costs of operations (approximately 75-80 percent of total cost) in spite of significantly declining populations and tax bases. As population continues to decline, the per capita cost for service increases. Decommissioning the excess, underutilized water infrastructure has the potential to reduce or stabilize these per capita service costs. A separate challenge impacting the wastewater system is reducing the quantity of runoff entering the combined sewer systems that are present in many shrinking cities. The quantity runoff may be reduced through decommissioning impervious surfaces and allowing the water to infiltrate the ground. Cities experiencing drastic urban shrinkage have the potential to shift land uses and selectively transition excess land from impervious to pervious surfaces to aid in meeting local, state, and federal regulations. The viability of decommissioning infrastructure components is examined for water infrastructure and wastewater infrastructure. EPANET, SWMM, L-THIA, and GIS are the primary tools used for analyses. A network analysis was performed using EPANET to examine how altering the topology of and changing demands within the network impacts the system’s performance using the metrics of adequate system pressures and fire flow capabilities. SWMM and L-THIA were used to evaluate the impact of decommissioning surfaces that contribute to runoff based on the percentage change in runoff from the status quo. The results yielded in SWMM and L-THIA were compared to estimate the differences in runoff quantities across different tools and assumptions. The metrics used and comparison of two tools allows for insight into decommissioning implications on system performance. Data incorporated in the models are gathered from city GIS databases and published literature. One Midwestern shrinking city that has experienced a loss of more than 40% of its population is used to demonstrate the feasibility of implementing the alternatives. The models were verified and validated by subject matter experts from Indiana and Michigan with backgrounds on issues inherent to shrinking cities, and water and wastewater infrastructure management or modeling experience. The results of the feasibility analysis for decommissioning water distribution pipelines and impervious surfaces illustrate the viability of proactively managing infrastructure, while providing adequate service levels, assisting in meeting regulations, improving aesthetics of the city, and potentially reducing or stabilizing service costs. By identifying issues inherent to shrinking cities and management options, city officials and decision makers are provided with insight that can be used to ensure effective and efficient long-term operation and management of water and wastewater infrastructure. Examining this new paradigm within cities of urban decline, aids in moving towards flexible infrastructure planning to accommodate future population trends, whether shrinking, static, or growing.

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2014 CRC PhD Student Poster Session

34. Monitoring Construction Progress at the Operation-Level using 4D BIM and Site Photologs Kevin K. Han (, Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign Measuring construction progress is an important indicator for project control. It provides practitioners with the information they need to easily and quickly detect performance deviations and decide on control actions that can avoid them or minimize their impacts. Nevertheless, current practices are costly, prone to error, and performed intermittently. To address these limitations, research has focused on creating methods based on laser scanning, image-based 3D reconstruction, or time-lapse photography together with Building Information Modeling (BIM). The common challenges in these emerging methods are 1) detecting operation-level progress in presence of static and dynamic occlusions which produce both missing or incomplete data and challenge reasoning about progress under limited visibility; 2) low level of development (LOD) in BIM where many elements only have a one-to-many relationship with the schedule activities; e.g., a 3D element corresponds to both "placement" and "waterproofing" activities; and 3) high-level Work Breakdown Structure in construction schedules where the operational details are missing. The over-arching objective of this work is to leverage 4D BIM and 3D point cloud models and create a new method for monitoring operation-level construction progress based on appearancebased classification of construction materials and formalized sequence of construction activities. A new appearance-based method for classification of construction material and inference of operation details and progress deviations is presented. The method leverages 4D BIM and 3D point cloud models generated from site photologs using Structure-from-Motion and Multi-View Stereo algorithms. In the developed system, the user manually superimposes these 3D models by assigning correspondences, and allows the photos and the 4D BIM to be automatically brought into alignment from all camera viewpoints. Through reasoning about occlusion, each BIM element is back-projected on all images that see that element. From these back-projections, several 2D patches per image are sampled and are classified into different material types. The image patches per element are then fed into a material classification machine learning based model and a quantized histogram of the observed material types is formed per element. The material type with the highest appearance frequency infers the state of progress. Based on spectrum of colors, the BIM elements – queried from a web database– are color-coded on a web-based platform enabling all project participants to be informed about the most updated state of the ongoing activities. For training material classification model, a new database containing 20 typical construction materials with more than 150 images per category was assembled and an average accuracy of 91% for 30×30 pixel image patches was reported. For progress monitoring, we assembled datasets of site imagery and BIM for two real-world under construction concrete structures. Our experiments shows that even in presence of static and dynamic occlusions, inaccuracies in sampling due to BIM-vs.-point cloud registration and presence or edges and corners as artifacts, our method is able to maximize visibility of elements by sampling a large number of image patches from different perspectives and correctly detect and report progress on all concrete elements. Our findings shows that it is feasible to sample and detect construction materials from images that are registered to point clouds and use that to infer the state of progress for BIM elements. The contributions are two-fold: First, a model-based reasoning method for operation level assessment of construction progress. This method leverages 4D BIM and image-based 3D point clouds and infers the state of progress using an image-based material classification method. Second, a publicly available dataset of incomplete and noisy point cloud models assembled from construction site images and BIM with different levels of detail for future analysis and comparison of image-based progress monitoring methods. This work has potential to enable projectby-project learning and improves basis of design and construction planning, as well as project control. Page 42 of 84

2014 CRC PhD Student Poster Session

35. Estimating Optimal Labor Productivity: A Two-Prong Strategy

Krishna Kisi ( and Nirajan Mani (, Advisor: Dr. Eddy Rojas University of Nebraska-Lincoln In an attempt to evaluate the efficiency of labor-intensive construction operations, project managers compare actual with historical productivity for equivalent operations. However, this approach towards examining productivity only provides a relative benchmark for efficiency and may lead to the characterization of operations as authentically efficient when in reality such operations may be only comparably efficient. Optimal labor productivity ⎯the highest sustainable productivity achievable in the field under good management and typical field conditions⎯ can provide an absolute benchmark for gauging performance. This optimal labor productivity level is lower than the productivity frontier ⎯the theoretical maximum achieved under perfect conditions⎯ because of system inefficiencies, which emerge due to factors outside the control/influence of project managers. Conversely, optimal labor productivity tends to be higher than actual labor productivity⎯ the productivity achieved in the field ⎯because of operational inefficiencies, which are the result of suboptimal managerial strategies. Estimating the magnitude of these system and operational inefficiencies will help project managers determine optimal labor productivity. This study develops a two-prong strategy for estimating optimal labor productivity. The first prong represents a top-down approach that estimates optimal labor productivity by introducing system inefficiencies into the productivity frontier. A Qualitative Factor Model is used to determine the impact of system inefficiencies. This top-down approach yields the upper level estimation of optimal labor productivity. The second prong is a bottom-up approach that determines optimal labor productivity by removing non-contributory work from actual productivity in a discrete event simulation. This bottomup approach generates the lower level estimation of optimal labor productivity. An average of the upper and lower limits reveals the best estimate for optimal labor productivity. The proposed two-prong strategy for estimating optimal labor productivity was successfully applied in a simple electrical installation project. The study analyzed actions performed by a veteran worker in “Fluorescent Bulb Replacement” task. The Qualitative Factor Model estimated 2.15 stations per hour as loss due to system inefficiencies. The discrete event simulation was found effective at modeling operational inefficiency. The loss due to operational inefficiencies estimated 0.96 stations per hour. The productivity frontier computed from this pilot study for the task was 20.23 stations per hour. Finally, from these data, the estimated value of optimal productivity was determined to be 15.92 stations per hour. Given that actual productivity was measured at 12.80 stations per hour, the task “Fluorescent Bulb Replacement” achieved an efficiency of 80%. Therefore, this pilot study demonstrates that the proposed methodology for estimating optimal labor productivity is adequate when applied to a simple electrical operation. This study contributes to the body of knowledge in construction engineering and management by introducing a two-prong strategy for estimating optimal labor productivity in labor-intensive construction operations and reporting on a pilot study performed to evaluate its feasibility using a simple electrical installation. Accurate estimation of optimal productivity allows project managers to determine the absolute (unbiased) efficiency of their labor-intensive construction operations by comparing actual vs. optimal rather than actual vs. historical productivity.

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2014 CRC PhD Student Poster Session

36. An Investigation of Occupant Energy Use Behavior and Interventions in a Residential Context Kyle Anderson (, Advisor: Dr. SangHyun Lee University of Michigan Across the globe, wide-scale efforts are being made to reduce energy consumption and carbon dioxide emissions. To date, innumerable efforts have focused on technological approaches to reduce the energy consumption of the building stock (e.g., installing efficient appliances); however, it is critical to remember that, in the end, all buildings are operated by humans, and how humans decide to behave in them has a significant and meaningful effect on their energy demand. Unfortunately, it is still not well understood how occupants choose to behave in buildings, what contributes to their behavior decisions, and what methods are effective at promoting lasting improvements in occupant energy use behavior. Therefore, the objectives of this research are as follows: 1) explore occupant behavior patterns to identify opportunities for improvement and barriers to change, 2) enhance our understanding of contextual factors that influence occupant energy use decisions, and 3) test the durability of novel behavior intervention strategies. In order to achieve these objectives, we have developed a multipronged research framework that employs computer modeling techniques with longitudinal field experiments and data collection. To better understand how occupants consume energy and the potential for behavior improvement, we collected hourly occupancy and energy use data of seven dormitory buildings, housing over 1300 rooms, for a year. This study used field-collected data to provide a first look into the amount of energy waste (energy used during periods of vacancy) that occurs in residential buildings. It was found that, annually, 21.5% of all energy consumed was spent during periods of non-occupancy. This is significant, not just due to its vast magnitude, but because it represents the amount of energy that can be saved by simple improvements in behavior without occupants having to forgo their comfort (e.g., lower heating set points). Further, it was found that no meaningful relationship exists between total energy consumption and the percentage of energy that was wasted in the room. High- and low-energy users alike wasted energy in proportion to their total consumption. These findings have significant practical implications as they imply that interventions can be designed to be highly non-particular—which places fewer demands on interveners—and still prove very effective given that energy waste is proportional across residents. Although this suggests that non-particular interventions can be very effective, applying interventions can still require significant effort, time, and expense. In addition, it is difficult to predict how effective a given intervention will be in one setting versus another, as contextual factors may be different. Therefore, researchers have begun developing computer models to simulate and analyze potential intervention outcomes to improve our understanding of how complex systems (e.g., social networks) can affect intervention success. In the previous literature, studies have just begun to explore the impact of network structure on the diffusion of social norms, and have evaluated only social network effects using limited social network structures and static social networks that are far from reality. Thus, to bridge this gap we evaluated how applying normative (i.e., social comparison–based) behavior interventions are affected by different social network structures and evolution using agent-based modeling. Results indicate that social network structure has a significant effect on the amount of time that interventions require to take effect and to reach steady state behavior, and on the variance in potential outcomes. Further, static social networks are much less volatile in their behavior and tend to have more convergent behavior relative to dynamic social networks which have greater amounts of grouping of behavior. This is a critical because, when proposing new interventions to facility or property managers, high levels of uncertainty in intervention outcome success, or failure, can provide a significant barrier to implementation. Findings from the proposed research contribute both in a practical and theoretical manner. First, the results from the studies add to our knowledge of pro-environmental behavior interventions. In addition, they enhance our understanding of how occupants behave in buildings and how contextual factors affect the spread of culture and behaviors. Second, the developed models help to advance the state-of-the-art simulations of behavior interventions, and provide interveners with new tools in determining which courses of action should be taken to meet sustainability targets.

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2014 CRC PhD Student Poster Session

37. Measuring the Complexity of Mega Construction Projects in China—a Fuzzy Analytic Network Process Lan Luo, Advisor: Dr. Qinghua He Tongji University The number of mega construction projects in China has considerably increased in the past decades. These projects are usually very complicated in nature, and understanding these complexities is critical to the success of these megaprojects. However, empirical studies related to the measurement of the complexity of megaprojects remain lacking. This paper reports the development of a complexity measurement model based on the Shanghai Expo construction project in China using the fuzzy analytic network process (FANP). First, a complexity measurement model consisting of 28 factors that are grouped under six categories, namely, technological complexity, organizational complexity, goal complexity, environmental complexity, cultural complexity, and informational complexity, is identified through the literature review and content analysis. Then, the model is refined by a two-round Delphi survey conducted in the aforementioned megaproject. Finally, a refined model, combined with suggestions for its application, is provided based on the survey results. The complexity measurement model based on the FANP can be used to determine the level of complexity of mega construction projects. These findings are believed to be beneficial for scholars and may serve as reference for professionals in managing megaprojects in China.

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2014 CRC PhD Student Poster Session

38. Decision Support System for Sustainable Labor Management in Masonry Construction Laura Florez (, Advisor: Dr. Daniel Castro-Lacouture Georgia Institute of Technology Masonry construction is labor-intensive. Its operations involve little to no mechanization and require a large number of crews made up of workers with diverse skills. Relationships between crews are tight and very dependent. Often tasks have to be completed concurrently and crews have to share resources and work space to complete their work. One of the problems masonry contractors face is the need to design crews, that is, determine the number of crews and the composition of each crew to be effectively used in the construction process to maximize workflow. This study proposes the framework (see Figure 1) for a decision support system to assist contractors in the allocation of crews in masonry projects. The proposed system can be a valuable tool to assist masonry contractors in the process of allocating workers while meeting worker’s needs and expectations.

Optimization module

Data input module

Reporting module

Planning horizon Quasi-ethnographic observations Availability of resources

Contractor's requirements -Rule 1 to Rule 7

Project Gantt chart

Optimization procedure

Workers' allocation

Masonry practices Workers' parameters: - Skills - Costs - Production rate

Labor consumption

Workers' needs

Figure 1. Optimization model framework

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2014 CRC PhD Student Poster Session

39. BIM-based Integrated Approach for Optimized Construction Scheduling under Resource Constraints Hexu Liu (, Advisor: Dr. Mohamed Al-Hussein, Dr. Ming Lu University of Alberta Building Information Modeling (BIM) has been recognized as a potential information technology with the potential to markedly change the Architecture, Engineering, and Construction (AEC) industry, and drew has drawn much attention from numerous scholars within the construction domain. Despite the reported advancements pertaining to BIM in previous studies, the extended use of BIM in automated construction scheduling has not yet reached its potential. In current practice, BIM in most cases functions as a database of 3D building components and provides only limited information (e.g., quantity take-offs) of each component for the downstream scheduling analysis, and rich building information embedded in BIM is not being utilized in order to facilitate the automatic generation of project schedules, entailing that massive manual work is required in the process of BIM-based project scheduling, especially in information exchanges between BIM modeling tools and scheduling tools. Any change of the project design or scope can lead to re-planning, an automated system is thus required to improve project planning efficiency. This research proposes BIM-based integrated scheduling approach which facilitates the automatic generation of optimized and detailed activity-level construction schedules for building projects under resource constraints, by achieving an in-depth integration of BIM product models with work package information, process simulations, and optimization algorithms. The integration is realized through the enriched information entity which extracts rich building product information of building components from BIM and WBS information from MS Access, and moves through the process simulation model in accordance with that enriched information, thereby yielding construction schedules. Meanwhile, evolutionary algorithms, such as particle swarm optimization, are also incorporated into the methodology to optimize the construction sequences with respect to the specified objective (e.g., minimum project duration). To implement the proposed methodology, a prototype system for scheduling panelized building projects is developed as an add-on tool for Autodesk Revit, and further is demonstrated by a case study. Building on the existing body of research in this field, the key contribution of this research is the integration of BIM product model with work package information, process simulations, and an optimization model, which provides solutions addressing the challenges of the existing practice with respect to automated scheduling of construction projects.

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40. Increasing mindfulness of coordination practices in inner city utility projects: the role of new (BIM) technologies Léon L. olde Scholtenhuis (, Advisor: Dr. T. Hartmann University of Twente Subsurface assets are nowadays owned, operated and reconstructed by distinctive organizations. This challenges the alignment of the many service providers (clients) and contractors involved in inner city subsurface reconstruction works. Although stakeholders negotiate and try to communicate while manually aligning their plans in meetings, absence of coordination hierarchy complicates these processes. Oftentimes, structures are missing to scope, explicate and formalize; and integrate plans effectively, resulting in suboptimal stakeholder alignment and error prone scheduling processes. To eventually enhance stakeholder alignment, this qualitative research evaluates how (BIM) technologies support practitioners’ focus on potential errors and holdups. To this end, we borrow the mindfulness-lens from the research domain of high-reliability organizing (cf. [1-6]). In this domain, mindfulness is defined as the organizational capability of being aware of discriminatory detail and capabilities to contain unforeseen circumstances [5]. To anticipate unforeseen situations, mindfulness’ anticipation principles, for example, suggest organizations to continuously spend attention to potential failures and to resist simplifying interpretations of reality. Additionally, mindfulness’ containment principles pursue organizations to defer to expertise in problem situations and build structures for resilience. We follow the iterative ethnographic-action research approach [7] to study how practitioners’ behavior changes along the mindfulness principles as we implement technologies. In our fieldwork we first observe current coordination practices, develop and implement new technologies - such as 4DCAD process visualizations – in multi-stakeholder project meetings. To analyze how the technologies influence mindful behavior we first tape- and video-record our fieldwork activities, and then use qualitative data analysis software to theorize from the observations. To date, we observed eight projects of which four where supported with 4D-CAD tools. In the first projects, we identified recurring coordination discussion topics and issues. Based on this we created a domain ontology that allowed us to develop customize 4D-CAD models in our four latest projects. In the last four projects, we observed how 4D-CAD provided visual, detailed overview of complex multi-stakeholder construction plans that are regularly hard to grasp in the mind. This supported clash and interface analyses, schedule shortcomings discussions, and evaluations of process delay. In terms of mindfulness these features strengthen behavior along pre-occupation with failures and reluctance to simplification principles. Also for containment, practitioners suggested that the tools could help to quickly evaluate delay mitigation plans, and therewith enhance resilience. We plan to continue our analysis on how tools such as 4D-CAD influence mindful coordination. Since our findings provide first evidence that 4D-CAD tools enhance mindfulness, and therewith, reduces errors and improve process reliability, we also plan to conduct valorization by providing customized 4D-CAD coordination trainings for utility project professionals. Additionally, we plan to show construction managers how possible (technology) innovation strategies can contribute to enhanced mindfulness in their projects. For practice, enhanced mindfulness eventually helps to increase stakeholder alignment and therewith reduces holdups and project overruns.

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2014 CRC PhD Student Poster Session

41. A systematic risk analysis approach against tunnel-induced building damages Limao Zhang (, Advisor: Dr. Xianguo Wu Huazhong University of Science and Technology Due to a continuous growth in urbanization worldwide, a large number of new tunnels are being constructed or planned for high-speed railways within congested urban areas, especially in developing countries, like China. The tunneling excavation works in soft ground inevitably lead to ground movements, which may cause adjacent surface buildings to deform, rotate, distort, and possibly sustain unrecoverable damages, especially those founded on shallow foundations. The exploitation of urban underground space presents several geotechnical engineering problems, one of which is the effect of underground tunnel excavation on surface and subsurface buildings. The impact of the tunnel excavation on adjacent buildings is of major interest for tunneling construction in urban areas, due to complex tunnel-soil-building interaction. In order to assure the safety and serviceability of nearby buildings in tunneling environments, it is necessary to explore the safety risk mechanism of the tunnelinduced damage to nearby buildings. The main works of this dissertation are as follows: (1) A static risk analysis model based on Extended Cloud Model (ECM) is proposed to analyze the safety of existing buildings in design phase. ECM is an organic integration of Extension Theory (ET) and Cloud Model (CM), where ET is employed to flexibly expand the variable range from [0, 1] to (-∞, +∞), and CM is used to overcome the uncertainty of fuzziness and randomness during the gradation of evaluation factors. An integrated interval recognition approach to determine the boundary of risk related intervals is presented, with both actual practices and group decisions fully considered. The risk level of a specific adjacent building is assessed by the correlation to the cloud model of each risk level. A confidence indicator is proposed to illustrate the rationality and reliability of evaluating results. Compared with other traditional evaluation methods, ECM has been verified to be a more competitive solution under uncertainty. (2) A dynamic risk analysis model based on Bayesian Networks (BNs) is proposed to analyze the safety of existing buildings in construction stage. At first, priori expert knowledge is integrated with training data in model design, aiming to improve the adaptability and practicability of the model outcome. Then two indicators, Model Bias and Model Accuracy, are proposed to assess the effectiveness of BN in model validation, ensuring that model predictions are not significantly different from actual observations. Finally, adopting the forward reasoning, importance analysis and background reasoning techniques in Bayesian inference, decision-makers are provided with support for safety risk analysis in pre-accident, during-construction and post-accident control processes in real time. (3) A decision support system, composed of sensing, processing and application layers, is presented to manager the safety of adjacent buildings in tunneling environments. The function design of the proposed support system is explored according to actual engineering requirements. This system is capable of realizing the risk monitoring, risk early-warning, and risk diagnosis for the safety assurance of adjacent buildings in real time throughout the overall tunneling works. The decision-making efficiency can be improved, and the dependence on domain experts existing in traditional expertisebased approaches is lowered, making efforts to perfect the safety monitoring and forewarning mechanism in tunnel construction. This system can be used by practitioners in the industry as a decision support tool to investigate the accident evolution patterns regarding the building safety in tunneling environments, as well as to increase the likelihood of a successful project in tunnel construction.

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2014 CRC PhD Student Poster Session

42. Modeling and Visualizing the Flow of Trade Crews in Construction Using Agents and Building Information Models (BIM) Lola Ben-Alon (, Advisor: Dr. Rafael Sacks Technion IIT In construction management research there is often a need for experiments to assess the impacts of different production control methods and information flows on production on site. Simulation methods are useful for such experimentation because field experiments suffer difficulties with isolating cause and effect. Existing methods such as DES (Discrete Event Simulation) are limited in terms of their ability to model decision-making by individuals who have distinct behavior, context and knowledge representation (Brodetskaia et al. 2012, Sawhney et al. 2003, and Watkins et al. 2009). The main objective is to develop an agent-based simulation model for studying and improving production control in construction processes, which accounts for individuals' decision making process and acquired knowledge. The simulation will exhibit the interdependence of individual workers and crews as they interact with each other and share resources. The goal will be to make the model robust and valid by attempting to calibrate it with field observations. Unlike the few existing research models, the simulation will be situated in a realistic virtual environment modeled using BIM, allowing future experimental setups that can incorporate human subjects. The method employs agent-based simulation (ABS), with a “bottom-up� approach to model the interactions between individual agents. It uses BIM models to define the physical and the process environment for the simulation. We apply agents programmed with decision making rules and utility functions to a to-be-built-environment represented as a BIM. By varying parameters such as reliability between workers, thresholds for information gathering and approach to making-do in terms of risk, it is possible to generate aggregate system performance similar to those found in an actual building context in the construction site. The research has two main steps: 1. Development of an agent based model to simulate the process incorporated in the LEAPCON game (LEAPCON 2005). This step has been implemented using the Starlogo TNG tool for multi-agent simulations which provides a development environment within a 3D visual context. The agent-based simulation of the LEAPCON game was developed with agents for the four independent specialty subcontractors, the client representative and the quality controller, and for each of the 32 apartments considered. The results show good calibration with existing observed field data, and to the existing DES. The effects are shown by measuring Work In Progress (WIP), Cycle Time (CT), cash flow patterns and efficiency of the operations (Sacks et al. 2007). 2. Development of an agent-based model in UNITY 3D game engine to simulate production control of a process in a full-scale building project. This step is being pursued in collaboration with a construction company. Data on workers' motivations, behavior and performance was collected using interviews and observations of a crew performing finishing works in a high-rise residential tower project. This step presented the following challenges: observation and formulation of the variables and target function (motivations) of the agents, modeling the behavior of the agents while classifying professions, validation by calibration with actual performance. The contribution of this research lies in the development and testing of the ABS simulation. No simulations of this kind exist: previous efforts with ABS systems for construction have been limited to simplified and virtual environments that use DES that cannot reliably model the complex, emergent patterns of production behavior that result from the interaction of the myriad subcontracting teams and suppliers that perform construction work on and off site. In particular, the influence of each participants' knowledge, context and motivations on their day to day decisions about resource allocation and work sequence can be modeled in the ABS simulations, while they could not be modeled using DES. Todate, there has been no simple and reliable way to test different ideas for production control paradigms in construction.

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43. Measuring Interdependent Infrastructure Resilience under Normal and Extreme Conditions MarĂ­a E. Nieves-MelĂŠndez (, Advisor: Dr. JesĂşs M. de la Garza Virginia Tech Infrastructure is one of the most important elements of our built society. Failure in the infrastructure system translates to human and economic losses, and in some cases environmental impacts that could last for decades. Therefore, it is imperative to put efforts in making sure that the lifeline systems are reliable in moments of stress. From the impact of natural hazards like Hurricane Katrina, to system failures like the 2003 Northeast blackout and component failures like the I-35W Minneapolis bridge collapse, civil engineers have the responsibility to renew and build infrastructure that can resist the impact of a disruptive event, respond to such impact in a timely manner, and recover to the normal operating condition. These three aspects form the concept of Resilience. A comprehensive literature review has revealed the necessity for further research that develop standard frameworks and techniques for measuring and increasing the resilience of the infrastructure systems. Consequently, the objective of this research effort is to develop a framework for measuring the resilience of a ground transportation system under normal and extreme conditions. Two existing models found in the literature will be combined with the purpose of measuring the resilience of roadways under predicted/anticipated traffic loads, routine intrusions, and extreme intrusions. The framework will integrate a probability approach with a three-stage (resistive, absorptive, and restorative) resilience model. This research can contribute to the development of techniques that identify the weaknesses in the system and find ways to increase the resilience. The results could help engineers, state DOTs and policy makers make investment decisions based on the resilience condition assessed. It could also help identify and bring awareness to the risks associated with failing to renew the infrastructure systems.

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2014 CRC PhD Student Poster Session

44. Thermally Activated Clay Based Biomass Pozzolana Investigations for Sustainable Construction in Ghana Mark Bediako (, Advisor: SKY Gawu and AA Adjaottor Kwame Nkrumah University of Science and Technology, Ghana In Ghana, as in much of Africa, cement for building construction is expensive. Agriculture and forestry biomass are perceived as a waste resource with little technological enhancement or valorisation. In most of farm-growing areas and the wood processing industries, waste biomass such as palm kernel shells, maize cobs, rice husk and sawdust have created environmental nuisance and disposal problems. On the other hand, the construction industry which depends enormously on cement also has negative impacts on sustainability through the generation of harmful anthropogenic gas and, major contributing factor to global warming. In this work, pozzolanic clay, which embodies less anthropogenic gas would be produced and use to replace cement using a readily available and local source. Waste biomass including palm kernel shells, sawdust, rice husks and maize cobs has been used as part of the raw materials for pozzolanic material production. Experimental program drawn for the investigation will include optimum temperature determination, chemical and mineralogical composition, mechanical and non-mechanical properties, morphological and phase analysis of hydrated products, heat evolution, durability, sustainability analysis and technology transfer program. Producing pozzolanic materials from a mixture of biomass and clay is a novel and this work is expected to expand alternative extended cements as well as alternative solutions to the disposal of waste biomass in Ghana.

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2014 CRC PhD Student Poster Session

45. ASSESSMENT OF ACTIVITIES’ CRITICALITY TO CASH-FLOW PARAMETERS Marwa Hussein Ahmed (, Advisor: Dr. Tarek Zayed, Dr. Ashraf Elazouni Concordia University Cash flow modeling is a very useful financial management tool that contractors use to run a sustained business. Contractors manage multiple activities within a single project. The activities’ start times are the inherent variables which determine the values of periodical negative cumulative balances and the other cash-flow parameters of cash flow model. This work reveals a system that can identify those activities that have the most influence on cash flow. The Monte Carlo Simulation technique has been employed here to generate schedules and their associated cash flow models for a case study by randomly specifying the activities’ start times. Uniform discrete probability distributions are assumed for the activities’ start times, with the minimum and maximum values representing the early and late start times, respectively. In addition to the randomness of the activities’ start times, the simulation model considered the stochastic nature of the periodic cash in and cash out transactions in the cash flow model by adjusting their values to account for the impact of 43 qualitative factors identified in an earlier study. The results are presented as probability distributions for the project duration, Profit , Financing cost and analyzed based on the three scenarios; each incorporating a different number of qualitative factors. The activities’ criticality to cash-flow parameters is assessed by evaluating the number of times a given activity determined a particular cash-flow parameter over the number of runs. This criticality measurement offers project managers very useful criteria with which to identify the activities that are most urgent to be completed on time and leads to a better accuracy of forecasting the cash flow parameters.

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2014 CRC PhD Student Poster Session

46. Understanding Current Horizontal Directional Drilling Practices in Mainland China Being Used For Energy Pipeline Construction Maureen Cassin (, Advisor: Dr. Samuel Ariaratnam Arizona State University As of 2009, China surpassed the United States as the largest global energy consumer. In 2013, coal, crude oil, and natural gas combined for over 90% of this consumption. Moving forward, it is estimated that China’s energy consumption needs will more than double by the year 2040, which has gained the attention of both the Chinese government as well as industry experts world wide. This projection has also illuminated the country’s ongoing and growing challenge to safely and efficiently supply energy resources across the country. This includes new supplies to vast areas with developing rural populations as well as to greatly increase supplies to rapidly growing urban areas. To meet China’s future energy needs, buried pipeline systems have become the most selected method of resource transmission. Pipelines are more significantly more cost effective in terms of both construction and operation than the alternative transmission methods of railway and long-haul trucking. In addition, construction of buried pipelines, particularly when trenchless construction methods are applied, have significantly less impact to the surrounding environment. Finally, buried pipelines are more sustainable than other transmission methods as they require fewer resources to construct as well as use less overall energy to operate than above ground methods. With this said, buried pipeline construction in China has become one of the country’s most influential industries. In recent years, the Chinese government has implemented massive buried pipeline construction initiatives, allowing China to become the fastest growing country in Trenchless Technology Methods. Horizontal Directional Drilling (HDD) has played a particularly critical role in executing these projects by providing an economical, timely and environmentally responsible method to bypass geographical challenges along the path of the pipelines. Understanding the role that Chinese HDD engineers and contractors have played in the overall development of China’s buried energy infrastructure would be of great value to the global trenchless technology community. Developing countries may benefit by Chinese advancements in large-scale HDD execution methods, while developed regions such as North America, may benefit by improving long established HDD practices. Thus, research of HDD’s critical role in the expansion of China’s energy infrastructure could have a positive effect on future buried pipelines practices and energy infrastructure construction.

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2014 CRC PhD Student Poster Session

47. Optimizing the Selection of Sustainability Measures for Existing Buildings Moatassem Abdallah (, Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign Buildings in the United States have significant impacts on the natural environment, national economy, and society. According to the U.S. Green Building Council, buildings in the United States account for 41% of energy consumption, 73% of electricity consumption, 38% of carbon dioxide emissions, and 14% of potable water consumption. Furthermore, aging buildings represent a significant percentage of existing buildings and are often in urgent need for upgrading to improve their operational, economic, social, and environmental performance. The owners of these buildings often seek to identify and implement building upgrade measures that are capable of improving building sustainability as well as achieving certification under various green building programs such as the Leadership in Energy and Environmental Design (LEED). Several green upgrade measures can be used to improve the sustainability of existing buildings such as energy-efficient lighting and HVAC systems, photovoltaic systems, and water-saving plumbing fixtures. Decision makers often need to select an optimal set of these building upgrade measures in order to maximize the sustainability of their buildings while complying with available upgrade budgets. The main goal of this research study is to develop single and multi-objective models for optimizing the selection of sustainability measures for existing buildings. To accomplish this goal, the research objectives of this study are to (1) evaluate the actual operational performance of sustainability measures in existing buildings, (2) develop a novel LEED optimization model that is capable of achieving user-specified certification levels with minimum upgrade cost, (3) develop an innovative environmental model for minimizing the negative environmental impacts of existing buildings, (4) develop an economic model for minimizing building life-cycle cost, and (5) develop a multi-objective optimization model that is capable of generating optimal tradeoffs among the building sustainability objectives of minimizing negative environmental impacts, minimizing upgrade cost, and maximizing LEED points. The computations of these optimization models were executed using genetic algorithms and quick energy simulation tool (eQUEST). The performance of the optimization models was analyzed and verified using case studies of public buildings. The results of analyzing these case studies illustrated the novel and unique capabilities of the developed models in searching for and identifying optimal sets of building upgrade measures for existing buildings. These new and unique capabilities are expected to support building owners and managers in their ongoing efforts to (1) achieve LEED certification, (2) reduce building energy and water consumption, (3) reduce building negative environmental impacts of greenhouse gas emissions, refrigerant impacts, mercury-vapor emissions, and light pollution, and (4) reduce building operational and life-cycle costs.

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2014 CRC PhD Student Poster Session

48. COMPETENCIES AND PERFORMANCE IN CONSTRUCTION PROJECTS Moataz Nabil Omar (, Advisor: Dr. Aminah Robinson Fayek University of Alberta In contemporary construction environments, construction companies tend to measure how well they perform against a set of predefined performance indicators. These performance indicators are assumed to be governed by the ability of the company to attain necessary sets of “competencies” that enables the successful execution of construction projects. Competencies in general are difficult to define and measure due to the multidimensional and subjective nature of its assessment. Competencies exhibit subjective assessments that cannot be expressed by the traditional numerical approaches. The body of literature conducted in the area of competencies and performance concluded a need for a more comprehensive research in this area to identify and formulate competencies and its relationship to construction projects performance. This research aims to: 1) identify and categorize the different sets of competencies and performance measurements necessary for better assessment of how well construction projects perform, 2) develop a methodology for measuring the different competencies and performance measurements, 3) map competencies to the different performance indicators, thus identifying possible enhancements for construction projects performance through identifying the relationship between competencies and performance, and 4) develop a fuzzy hybrid intelligent model to predict construction project performance based on the existing competencies on the project level. The methodology for conducting this research is divided into three stages namely; 1) identifying the different competencies and performance measurements through literature review and expert’s focus groups, 2) collecting competencies and performance measures from different construction projects in Alberta, Canada, and, 3) applying novel fuzzy-hybrid techniques for analysis and modeling of competencies and performance. This research is expected to have an ample scientific and practical significance. On the scientific level, this research will investigate and apply state of the art techniques in fuzzy-hybrid modeling through the application of prioritized fuzzy aggregation for combining experts’ evaluation for the different competencies. A cascade fuzzy neural network will be developed for predicting project performance based on existing competencies. On the practical level, a process for identifying and measuring project competencies and predicting project performance will be available for construction practitioners to assess their project competencies and how well construction projects are executed. Additionally, the prediction model to quantify competencies and forecast different performance indicators will function as a decision-support tool for construction practitioners when evaluating construction projects performance.

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49. Improving Construction Cost Escalation Estimation Using Macroeconomic, Energy and Construction Market Variables Mohsen Shahandashti (, Advisor: Dr. Baabak Ashuri Georgia Institute of Technology Recently, the accuracy of construction cost estimates has been significantly affected by fluctuations in construction costs. Construction cost fluctuations have been larger and less predictable than was typical in the past. Cost escalation has become a major concern in all industry sectors, such as infrastructure, heavy industrial, light industrial, and building. Construction cost variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owner organizations. The major problem is that construction cost is subject to significant variations that are difficult to estimate. The objective of this research is to create multivariate time series models for improving the accuracy of construction cost escalation estimation through utilizing information available from several indicators of macroeconomic condition, energy price and construction market. An advanced statistical approach based on multivariate time series analysis is used as a main research methodology. The first step is to identify explanatory variables of construction cost variations. A pool of 16 candidate (potential) explanatory variables is initially selected based on a comprehensive literature review about construction cost variations. Then, the explanatory variables of construction cost variations are identified from the pool of candidate explanatory variables using empirical tests including correlation tests, unit root tests, Granger causality tests, and Johansen’s cointegration tests. The identified explanatory variables represent the macroeconomic and construction market context in which the construction cost is changing. Based on the results of statistical tests, several multivariate time series models are created and compared with existing models for estimating construction cost escalation. These models take advantage of contextual information about macroeconomic condition, energy price and construction market for estimating cost escalation accurately. These multivariate time series models are rigorously diagnosed using statistical tests including Breusch–Godfrey serial correlation Lagrange multiplier tests and Autoregressive conditional heteroskedasticity (ARCH) tests. They are also compared with each other and other existing models. Comparison is based on two typical error measures: out-of-sample mean absolute prediction error and out-of-sample mean squared error. Based on the correlation tests, unit root tests, and Granger causality tests, consumer price index, crude oil price, producer price index, housing starts and building permits are selected as explanatory variables of construction cost variations. In other words, past values of these variables contain information that is useful for estimating construction cost escalation. Based on the results of cointegration tests, Vector Error Correction models are created as proper multivariate time series models to estimate cost escalation. Our results show that the multivariate time series models pass diagnostic tests successfully. They are also more accurate than existing models for estimating cost escalation in terms of out-of-sample mean absolute prediction error and out-of-sample mean square error. These findings contribute to the body of knowledge in construction cost escalation estimation by rigorous identification of the explanatory variables of the escalation and creation of multivariate time series models that are more accurate than the univariate time series models for estimating the escalation. It is expected that proposed cost escalation estimation models enhance the theory and practice of cost escalation estimation and help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects in various industry sectors.

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50. Ex-Ante Simulation and Visualization of Sustainability Policies in Infrastructure Systems: A Hybrid Methodology for Modeling Agency-User-Asset Interactions Mostafa Batouli (, Advisor: Dr. Ali Mostafavi Florida International University The research presented in this poster focuses on the creation and testing of a new paradigm for sustainability assessment in urban infrastructure System-of-Systems (SoS). The National Academy of Engineering recently listed “restoring and improving urban infrastructure� through sustainable approaches as one of the global challenges of engineering in the 21st century. Assessment of sustainability in infrastructure systems is complex due to the existence of various actors whose adaptive behaviors and interactions affect the performance of asset networks. However, the existing methodologies (e.g., urban metabolism and life cycle analysis) for assessment of sustainability in infrastructure systems do not capture the existing complex adaptive behaviors and uncertainties, and thus, could not provide a robust basis for policy analysis and decision-making. The key missing element is an integrated methodology that captures the complex interactions at the interface between agency, asset, and user behaviors for ex-ante analysis of sustainability in infrastructure systems. The objective of this study was to create and test an ex-ante analytical framework for microsimulation of policies related to the sustainability of urban infrastructure under uncertain conditions. This research investigated the hypothesis that sustainability in infrastructure System-of-Systems is an emergent property as a result of the coupling effects between: (1) the strategic and operational decision-making processes of the asset owners, (2) the performance of infrastructure assets, and (3) the user behaviors. A System-of-Systems approach was adopted in this study to provide an integrated framework for analysis of sustainability in infrastructure systems. This framework was based on the abstraction and micro-simulation of the interactions between the dynamic behaviors of infrastructure agencies, users, and assets, and its application was demonstrated in assessment of the sustainability in highway transportation infrastructure. Using the framework and data obtained from different sources ranging from historical records and literature reviews to case studies, the interdependencies between agency, asset, and user behaviors were explored. These interdependencies (e.g., the effects of maintenance/rehabilitation decisions of agencies on asset performance and user behaviors) were then used to develop an integrated model for micro-simulation of policies related to sustainable infrastructure management. In the integrated model, the micro-behaviors of infrastructure agencies and users were captured using agent-based modeling, the dynamic performance of infrastructure assets were modelled using system dynamics, and the environmental impacts were considered using a performance-adjusted life cycle analysis model. Using this model and Monte-Carlo experimentation, the policy landscape pertaining to the sustainability of a highway transportation network was simulated in a case study. The model was verified and validated by using sensitivity analysis and uncertainty propagation analysis. The results revealed the optimal policy scenarios based on different levels of budget, pavement types, and maintenance/rehabilitation strategies that enhance the sustainability of infrastructure systems at the network level and under different uncertain conditions. This distinctive approach is the first of its kind to simulate and visualize the policy landscape pertaining to the sustainability of infrastructure systems by simulating the dynamic behaviors at the interface between agencies, users, and assets. The framework and simulation model have the following benefits for policy analysis: (i) simulation and visualization of the outcomes of policies on the sustainability of infrastructure at the network level and at various policy horizons, (ii) comparison of the outcomes of different policies based on different infrastructure characteristics, agency priorities, and user behaviors, (iii) creation of the landscape of sustainable policies for infrastructure management, (iv) identification of the desired scenarios, and (v) quantification of the likelihood of desirable outcomes as a result of different policies.

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51. Dynamic Fatigue Model for Assessing Muscle Fatigue During Construction Tasks MyungGi Moon ( Advisor: Dr. SangHyun Lee University of Michigan Construction is a labor-intensive industry that employs 11.1 million workers in the U.S., and that involves repetitive manual handling works. Construction workers therefore frequently suffer from a significant level of fatigue that heavily affects their physical capability. In particular, work-related muscle fatigue can result in various adverse effects, such as productivity loss, human errors, unsafe acts, muscle injuries, and work-related musculoskeletal disorders (WMSDs). Also, fatigue related to the nervous system (i.e., central fatigue) can result in long-term performance decline, such as chronic fatigue syndrome, burnout syndrome, and absenteeism. There have been diverse research efforts attempting to understand and quantify fatigue. However, they have been conducted under constrained laboratory conditions or are mainly based on surveys, instrumental methods, and mathematical models. As a result, these research efforts may not be suitable for assessing occupational fatigue under real work conditions due to limitations in laboratory experiments, the possibility of bias in surveys, interference with ongoing work in instrumental methods, and the difficulty in reflecting dynamic workloads and diverse task demands in mathematical models. To address these issues, we aim to estimate fatigue during construction tasks by applying System Dynamics (SD), Discrete Event Simulation (DES), and biomechanical analysis using 3DSSPP. SD is a simulation technique that helps to understand feedback processes by identifying variables’ causeand-effect relationships. DES is used to represent a certain construction task (e.g., masonry and rebar works) showing sequential work elements and providing idling time and work time. Captured workers_ motion data using a Kinect will provide internal force data (% of Maximum Voluntary Contraction: MVC) loaded at the workers_ body parts using 3DSSPP. As a result, localized muscle fatigue can be estimated by the SD model as a function of construction workloads generated by 3DSSPP from different construction tasks with an interval for force exertion (e.g., working and idling time) from DES. The proposed model was statistically validated by comparing it with existing fatigue models in terms of Normalized Root Mean Square Deviation (NRMSD). In addition, experimental validation was conducted by measuring endurance times, one of the measures for muscle fatigue, from subjects under several force exertion protocols that mimic construction masonry tasks (i.e., repetitive lifting concrete masonry units); the endurance times were then compared with estimated endurance times from our model. The results show that the model provides a robust estimation of localized muscle fatigue. This research greatly contributes to an understanding of physiological demands during construction tasks, and identifies whether and to what extent fatigue can be estimated and quantified for actual tasks. Further, the model can be used to adjust work schedules, providing a test tool to estimate workers_ fatigue and eventually preventing undesirable consequences from muscle fatigue by extending the margin of safety.

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52. Toward Sustainable Capital Transportation Infrastructure: Maximizing Performance of Preplanning Phase Nahid Vesali (, Advisor: Dr. Mehmet Emre Bayraktar Florida International University The need for new and updated infrastructure has grown greatly all around the world in the last decades. Among all infrastructures, Capital (large-scale) infrastructure projects draw more attention because of their considerable investment and inherent complexity. One of the challenges face to the capital infrastructure projects is finding potential solutions and identifying best-fitted option to respond the investigated need or problem while considering three pillar sustainability issues. These processes happen in preplanning phase of the project. Most of capital transportation infrastructure projects experience long time preplanning phase, for example in Port of Miami Tunnel project, preplanning phase take about ten years and selected solution alternate several times. This indicates that there are deficiencies in this early stage phase, which need to be determined and improved. This study creates a novel decision-making model for preplanning phase in capital transportation infrastructure. This research reveals the deficiencies and pitfalls in preplanning phase of capital transportation infrastructures and finds solutions to overcome them. Three main deficiencies are determined here: 1) A transparent, formal and systematic procedure is rare for preplanning phase in large-scale projects; 2) Project level uncertainty is not considered in preplanning phase and alternative appraisal; 3) Selecting the alternatives is often based on subjective expert opinions and impact of required cost and time for investigating alternatives in different level of uncertainty is not considered. To achieve the purpose of the study, first the mechanism of preplanning phase of capital transportation infrastructure is established and broke down to some sub-phases to find the dynamics of the phase, which are: Identifying need for project and problem analysis; Creating ideas and defining scope; Preliminary feasibility study and alternatives appraisal; Select the optimal alternative. The conceptual sub-phases identified based on the literature review, analysis of ten implemented capital transportation infrastructures and interviews. Then the important factors affecting the alternative selection process in each sub-phase are determined. One considerable problem highlighted in feasibility study sub-phase is dealing with high uncertainties due to lack of detailed data. To cope with this problem, a project level uncertainty assessment with Monte Carlo simulation model is established to convert the traditional deterministic feasibility study into probabilistic results. The outcome of this stage is a matrix of distribution function of measured value of factors for each alternative. Finally, the study seeks a holistic framework to find the optimal alternative of capital transportation infrastructure by the end of preplanning phase. A decision support system, which is an optimization model, is proposed. The model compares different alternatives based on aforementioned distribution function of distinct factors, in the form of probabilistic decision tree objective analysis. Each decision maker organization or governmental agency can enter its priorities among factors to the model and find the optimal solution with specific confidence level. Using this model significantly decreases the complexity as well as required time and cost of preplanning phase in capital transportation infrastructures.

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53. A quantitative investigation of building micro-level power management through energy harvesting from occupant mobility Neda Mohammadi (, Advisor: Dr. Tanyel Bulbul, Dr. John E. Taylor Virginia Tech Climate change mitigation strategies are targeting carbon pollution reduction by at least 3 billion metric tons cumulatively by 2030. To comply with this end particularly in our residential and commercial sectors, a correlation between the energy efficiency strategies and energy harvesting from emerging renewable energy resources is essential. Due to high reliance on electricity in energy demands, power management plays a significant role in the required interplay between the two strategies. While macroscale energy harvesting technologies such as wind turbines, hydro-electric generators and solar panels are being employed in macro-scale power management by directly feeding the grid; we lack an integrated micro-scale power management system at the building level which relies on energy harvesting from ambient environment. We intend to explore an alternative micro-scale energy harvesting system which can be employed in buildings in support of off the grid micro-level power management. Harvesting, converting, and storing energy from human locomotion through wearable devices have attracted commercial and military attention; but have largely focused on relatively small scale energy conversion for personal devices. In this research, we quantify whether the accumulated energy harvested from building occupants’ mobility can contribute to the electrical demand, and thus offset CO₂ emissions. We conducted a pilot study in which the data from wearable activity tracker devices was monitored to assess the potential available energy which could be transmitted to balance the energy consumption of an office building. Office buildings have high occupant mobility in aggregate which could offset a meaningful portion of building CO₂ emissions. An energy balance analysis incorporating the electrical energy harvested from occupants’ mobility has shown promising results of more than 2 tons of CO₂ emission reduction in one month for the office building under study. This amount in larger scales can potentially offset meaningful portions of disaggregated energy use and its consequential emissions. We offer an exploratory analysis of the potential energy conversion and exchange in a buildingoccupant system which can be produced through energy harvesting of human locomotion. Harvesting energy generated by the human mobility patterns of building occupants may represent an important step forward in instigating a larger renewable energy resource to draw upon, which will also infuse the occupants' network with improved energy consumption behavior.

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54. Estimating Labor Productivity Frontier: A Pilot Study Nirajan Mani ( and Krishna P. Kisi (, Advisor: Dr. Eddy M. Rojas University of Nebraska-Lincoln The efficiency of construction operations is typically determined by comparing actual vs. historical productivity. This practice is accurate if historical data reflect optimal values. Otherwise, this comparison is a gauge of relative rather than absolute efficiency. Therefore, in order to determine absolute efficiency, one must compare actual vs. optimal productivity. Optimal labor productivity is the highest sustainable productivity level achievable under good management and typical working conditions. Meanwhile, labor productivity frontier is the theoretical maximum production level per unit of time that can be achieved in the field under perfect conditions. This level of productivity is an abstraction that is useful in the estimation of optimal productivity for labor-intensive operations. This paper reports on a pilot study performed to evaluate the feasibility of a dual approach for estimating the productivity frontier for a simple electrical installation. The first approach involves the estimation of the productivity frontier by using observed durations from a time and motion study. The movements of a worker are captured by multiple synchronized video cameras. The actions that make up a particular task are identified from the video frames and categorized into contributory and noncontributory actions. The best series of contributory actions are identified based on the shortest time taken to complete a task, and a synthetic worker model is used to determine the productivity frontier as the sum of the shortest durations for each action. The second approach involves the estimation of the productivity frontier by using estimated durations on the same time and motion study. The probability distributions that best represent action durations are identified and the productivity frontier is defined as the sum of the lowest values from each of the distributions at a 95% confidence interval. The highest labor productivity value from these two strategies is taken as the best estimate of the productivity frontier. Thus, the labor productivity frontier computed from this pilot study for the “Fluorescent Bulb Replacement� task is 20.23 stations per hour. An accurate estimate of labor productivity frontier is the first step toward developing a process that will allow project managers to determine the efficiency of their labor-intensive construction operations by comparing actual vs. optimal rather than actual vs. historical productivity. Toward this end, this paper reviews relevant literature, presents the details of the proposed dual approach, introduces results from the pilot study, and evaluates the feasibility of this methodology for estimating the labor productivity frontier.

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55. A Decision Support System for Sustainable Multi Objective Roadway Asset Management Omidreza Shoghli (, Advisor: Dr. Jesus M. de la Garza Virginia Tech The limited and constrained available budget along with old aging infrastructure in nation magnifies the role of strategic decision making for maintenance of infrastructure. The challenging objective is to maintain the infrastructure asset systems in a state of good repair and to improve the efficiency and performance of the infrastructure system while protecting and enhancing the natural environment. Decision makers are in need of a decision support system to consider these multiple objectives and criteria. The research proposes and validates a framework for such decisions. Appropriate and optimal maintenance actions will maintain infrastructure at the best possible condition by investing the minimum amount of money while considering environmental and time constrains. The proposed model aims at finding optimal techniques for maintenance of multiple roadway asset items while taking into account time, cost, level of service and Green House Gas (GHG) emissions. Therefore, goal is to answer what are the optimal combination of maintenance techniques for roadway assets to maintain four conflicting objectives of time, cost, level of service and GHG? In other words, the main objective is to develop a decision support system for selecting and prioritizing necessary actions for MR&R(Maintenance, Repair and Rehabilitation) of multiple asset items in order for a roadway to function within an acceptable level of service, budget, and time while considering environmental impacts. This model creates a framework for a sustainable infrastructure asset management. A two stage approach: First a multi-objective problem with four main goals is analyzed. The objectives of the problem are: minimization of maintenance costs, minimization of maintenance time, Minimization of GHG and Maximizing level of service. In the second stage, the results of the multi objective optimization will be further processed using a Multi Criteria Decision Making (MCDM) process. There have been many studies reported in the literature presenting the application of decision-making techniques for individual asset items. However, no previous attempts have been made to develop a decision support system for asset management to select optimal maintenance for various asset items. The proposed approach will simultaneously optimize four conflicting objectives along with using multi criteria decision-making technique for ranking the resulted non-dominated solutions of multi objective optimization. The results of implementation of the proposed model on a section of I-64 are presented for sub-set of asset items. Moreover, the proposed model is verified and validated using two more projects. Results reveal the capability of the model in generation of optimal solutions for the selection of maintenance strategies. The results of the research benefits decision makers by providing them with optimal solutions for infrastructure asset management while meeting national goads towards sustainability and performance-based approach. Moreover, provides a tool to run sensitivity analysis to evaluate annual budget effects and environmental impacts of different MR&R resource allocation scenarios. Application of proposed approach is implemented on roadway asset items but it is not limited to roadways and is completely applicable to other infrastructure asset.

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56. SimulEICon: A Simulation-based Multi-objective Decision-support Tool for Sustainable Building Design Peeraya Inyim (, Advisor: Dr. Yimin Zhu, Dr. Wallied Orabi Florida International University The significance of sustainable construction in the architecture-engineering-construction (AEC) industry has been identified and emphasized in a wide range of research. Conventional construction projects are complex endeavors with many professionals and parties from different disciplines trying to meet multiple project objectives that are often different and conflicting. This complexity is compounded by new requirements for sustainable construction. SimulEICon or Simulation of Environmental Impact of Construction is a simulation-based tool that can aid construction professionals in the decision-making process during the design phase of a building. This tool optimizes the selection of materials, components, and construction methods. Currently, SimulEICon is built in MATLAB environment in order to take advantage of its functions and toolboxes and it considers three main objectives, which are construction time, cost, and environmental impacts, in term of carbon emissions, throughout the life cycle of buildings. SimulEICon uses Monte Carlo simulation to account for uncertainty and availability of data, and uses energy simulation to estimate environmental impact of energy consumption during operating phase. Furthermore, the search for optimal design solutions at the building level entails the consideration of millions of possible solutions; genetic algorithms are used as optimization technique. SimuEICon can present a wide range of possible optimal or near optimal solutions from which construction professionals may choose the most appropriate solution to meet project goals.

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57. Construction Operations Process Data Modeling and Knowledge Discovery Using Machine Learning Classifiers Reza Akhavian (, Advisor: Dr. Amir H. Behzadan University of Central Florida Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup and calibration are still major prohibitive factors in field data sensing. The ubiquity of mobile devices equipped with a range of onboard sensors facilitates the emergence of context-aware applications. Notwithstanding their applications in computer sciences, healthcare, and sports, such pervasive technologies have not been yet fully investigated in construction and facility management domains. The presented research explores the potential of built-in sensors of mobile devices in providing process-level data that can lead to operations-level knowledge discovery from construction resources. In particular, smartphone sensors are used to capture multi-modal data for equipment action classification and recognition. Sensory data is collected in two modes: controlled (i.e. instructed) and uncontrolled environments. In all scenarios, the device is placed inside the equipment cabin over a near field communication (NFC) smart tag to launch the data logger application. Raw data collected by built-in sensors such as 3-axis accelerometers, gyroscope, and global positioning system (GPS) are used to extract key time- and frequency-domain features such as mean, peak, standard deviation, correlation, energy, and entropy in windows with different sizes with 50% overlap. Feature normalization and necessary dimensionality reductions will be performed on the resulting features in the preprocessing stage. Machine learning classifiers are then employed to classify the features for detecting various construction equipment actions and estimating durations of different activities. Also, 10-fold cross validation and paired t-test are used to evaluate the accuracy of the classifiers in detecting activities. This is done for both controlled and uncontrolled modes; in the controlled mode, all possible actions with a few second intervals in between performed by different construction equipment are annotated to label the classified actions, whereas in the uncontrolled mode, equipment perform a certain activity which may consist of multiple actions. Preliminary data collected from different classes of construction equipment performing various actions shows certain repetitive and distinguishable patterns in collected data while the equipment performs particular activity. The output of the developed algorithms can contribute to the current practice of construction simulation input modeling by providing knowledge such as activity durations and precedence, and site layout. The resulting data-driven simulations will be more reliable and can improve the quality and timeliness of operational decisions. Moreover, the action recognition framework can be used for field safety improvement, productivity assessment, and equipment emission monitoring and control.

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2014 CRC PhD Student Poster Session

58. The Development of an Automated Progress Monitoring and Control System for Construction Projects Reza Maalek (, Advisor: Dr. Janaka Ruwanpura, Dr. Derek Lichti University of Calgary Project Monitoring and Control are vital to facilitate decision makers identify deviations between the as-planned vs. asbuilt state of the project and take timely measures where required. Monitoring is the process of collecting onsite data as a means of measuring the performance of the project. Traditionally, onsite data are collected manually, a time consuming and labor intensive task particularly in large scale projects. In practice, to justify the time and cost associated with such manual approaches, a limited amount (or frequency) of onsite data are collected, which diminishes the ability of the project manager to identify the causes of delays and cost overruns on time. Currently, site supervisory personnel spend 30-50% of their time on manually monitoring and controlling onsite data. Therefore, a novel approach towards data collection and analysis is required to help overcome the aforementioned limitations of current manual monitoring and control practices. Here, the main objective is the development of an automated monitoring and control system to assess the performance of construction work as-progressed and to predict the stochastic outcomes of the project. The proposed automated monitoring and control system consists of the following stages: 1. Automated Monitoring System: The technology capable of collecting the “scope of the work performed” is of interest. Based on the comparative evaluation of the applicable remote sensing technologies presented in, LiDAR (Light Detection And Ranging) is recommended for construction site monitoring. With respect to the nature of LiDAR data, the following three concerns are required to be addressed: (i) Optimization of the Location of the Scan-Stations: One of the goals of this research is to reduce the time and cost of manual monitoring. Therefore, the minimum number of scan stations capable of providing 3D point clouds of every structural element onsite is of the essence. The as-planned 4D model is used to simulate point clouds starting from a scan station positioned at an arbitrary location. The scanner is then moved in increments of “fuzzy” terminology and the expected point clouds are simulated. The location where the maximum number of structural facets are detected is considered as the initial scan station. The facets corresponding to the initial scan station are then removed from the as-planned model and the process is repeated for the remaining surfaces until a point cloud is assigned to every surface. (ii) Automated Feature Recognition: This stage involves the automatic identification of structural elements (i.e. Column, Slab) from the collected unorganized LiDAR point clouds. Current object-based recognition models use the planned model as a-priori knowledge to assign 3D point clouds to a structural element [69], which may not be reliable in cases where the location of the as-built structure differs from the planned location. In order to eliminate the dependency of the feature extraction model on the as-planned data, the “Geometric Primitives” are used to detect planar (Wall, Beam, Floor and Ceiling Slabs) and cylindrical (Column, Pipe, Cable, Reinforcement) surfaces. To reduce the effects of outliers caused by occlusions, moving objects and dust, a Robust method of Principal Component Analysis (PCA) is proposed to extract planar features through a robust estimate of the covariance matrix of a neighborhood of each point cloud (iii) Automated Feature-based Registration: Since the as-planned model is georeferenced to a specific coordinate system during the feasibility stages, it is important to register the as-built data to the same coordinate system. For this matter, at least three (3) non-collinear point correspondences between the as-planned and as-built models are required. The extracted features are used to identify the point to point correspondences in order to perform a rigid body transformation from the scanner space to the as-planned space. 3. Automated Control System: The identified “scope of the work performed” is compared to the “scope of the work planned to be performed” in order to determine deviations between the planned and the actual state of the project. Two types of analysis are then performed where significant differences are detected. Initially a Stochastic Neural Network based Earned Value (EV) analysis is carried out to well predict the expected time and cost of completion of the project. Through project management performance enhancement tools such as Crashing, an optimized decision support solution is introduced to improve productivity. To evaluate the feasibility the proposed method for automatic development of 3D as-built models, one set of LiDAR data from a laboratory at the University of Calgary was collected. Our proposed method was able to detect the 22 walls in laboratory with a Mean Radial Spherical Error (MRSE) of 9 cm. Another set of experiment is also designed to monitor and control the expansion of the School of Engineering project for a duration of one year. The main contribution of this research is an automated construction progress monitoring and control system to improve time, cost and quality of the state-of-the-art onsite monitoring practices and to produce the opportunity for timely identification of deviations between the as-planned and as-built state of the project. The following benefits to the construction industry are denoted with the efficient implementation of the aforementioned system: (i)- reduction of the project managers' time and cost of travel to and within the site; (ii)- improving time, cost and reliability of data collection and analysis; (iii)- reduction of time and cost of preparation and analysis of progress reports; (iv)- improvement of quality inspection and management in order to minimize rework early in the project lifecycle; (v)- minimizing construction claims (vi)- development of 3D/ 4D as-built models of the construction site; and (vii)- stability control and Health monitoring of structural elements.

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59. Optimum Resource Utilization Planning in Construction Portfolios through Modeling of Everyday Uncertainties at Certain Confidence Level Reza Sheykhi (, Advisor: Dr. Wallied Orabi Florida International University Planning of resource utilization can largely affect construction completion time and cost, especially when everyday uncertainties are taken into account as main sources of unexpected changes during projects. The impact is even more significant when managers should plan to supply limited pool of resources to a portfolio of concurrent projects, such as transportation network reconstruction. However existing studies in resource-constrained planning did not capture impact of day-to-day changes on time-related risk factors (e.g. weather, and trade coordination, etc.) and their associated uncertainties, and therefore, could not provide a robust and realistic basis for decision-making. On the other hand, planning of a group of project competing for limited pool of resources requires planners to examine their alternative resource sharing capabilities and policies under uncertainty, which is another important missing element in reported construction research. The objective of this research is to cover mentioned research gaps through developing a model to capture impacts of daily-changing uncertainties and alternative resource utilization policies on completion time and cost of construction project portfolios. This study investigates the hypothesis that such a model can result in (1) a more realistic trade-off between completion time and cost and (2) more distinctive and applicable optimal resource utilization solutions for resource-constrained portfolio planning in construction. Developed model aims to provide planners with a contemporary solution map of portfolio planning outcomes (time and cost) based on their resource sharing capabilities and restrictions, and with respect to their risk-taking confidence. This research adopts two main approaches to achieve mentioned objectives: (1) modeling of daily-changing uncertainties in construction network, through stochastic simulation of day-to-day changes in crew productivity instead of activity duration, and (2) finding optimal portfolio time-cost trade-off through a multi-objective optimization model. To this end, Monte Carlo Simulation Method has been employed to capture stochastic nature of crew productivity and to develop portfolio completion time and cost distributions. In addition, the NSGA-II multi-objective optimization has been implemented to find optimum solutions based on (1) alternative project prioritizations (in order to consume limited resources), and (2) alternative overtime working policies. The optimization model eventually intends to minimize overall time and cost of the portfolio, which are obtained from distributions based on planners predefined confidence level. The developed model has been employed for modeling reconstruction of a real case study of 5 roadway projects within a portfolio in United States. The resulting solution map (1) verifies model results being more reliable and realistic comparing to deterministic planning of construction, and (2) enables decision makers to pick their desired optimum solution for resource planning based on their actual availabilities and desired risk-taking confidence level. Results suggest that using alternative overtime policies may vary portfolio duration and cost up to 50% and 5%, respectively. However, using overtime policy alternatives with lower productivity adjustments does not necessarily result in longer projects, and thus the amount of weekly performance of crews should be considered in estimation of total duration. It is also found that higher weekly performance per unit expenditure (applying regular overtime working policies) results in longer portfolios with lower total cost under uncertainties. This research, in general, helps planners select their preferred combination of resource planning options to achieve optimal completion time and cost under uncertainty. To this end, the model provides planners with practical decision support material in order to answer the following questions: (1) how much of each resource should be available in work periods, such as weeks, and (2), how to share this limited available resource pool among projects. This research is the first of its kind (1) to capture impacts of daily-changing uncertainties and alternative resource utilization policies on completion time and cost of construction, and (2) to model planning of a group of project competing for limited pool of resources by examining alternative resource sharing capabilities and policies.

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2014 CRC PhD Student Poster Session

60. Using STEP Approach to Achieve Successful Outcomes on Complex Projects Ron Patel (, Advisor: Dr. Edward J. Jaselskis North Carolina State University The basic underlying issue of this research relates to complex projects not achieving their desired level of performance in the areas of operational success, cost, schedule, and safety. Complex projects are unique in many respects compared to more traditional ones by nature of their more diverse stakeholder objectives, complicated organizational structures, unique financing arrangements, unproven technologies, and challenging project locations. This research organizes leading project management practices through a STEP approach in an easy to follow guide for project stakeholders to achieve better project performance on complex projects. STEP stands for Strategy, Team, Execution procedures, and Performance Monitoring; this approach embodies much of the research that has been performed in the area of construction project success on complex projects. First, developing the right Strategy creates a stable foundation for achieving successful complex project performance. This involves selecting the right project delivery approach, contract type, and understanding the risks and uncertainties and developing plans to proactively deal with them. Selecting the right Team involves building a cohesive organization with the requisite level of knowledge, experience, and size in terms of number of people and hierarchy. Executing industry proven standard work process procedures are also important as they that provide consistency to the Team in terms of how its members interact with one another and how work is to be accomplished. Lastly, Performance monitoring using appropriate metrics is accomplished throughout all phases of the project to ensure that the project is tracking according to plan. The STEP procedure was investigated from different phases of the project life-cycle. The research methodology includes two stages: the first stage involves building the STEP model. Literature review on existing research pertaining to project success on complex projects and leading project management practices are used to build the STEP model. The second stage involves validation of the STEP approach. First, a questionnaire design and expert survey is planned to ensure these guidelines have included all relevant factors in the model. Second, a case study approach will be implemented to correlate attributes found in the STEP approach to project outcome. These guidelines and procedures can be used by owners and contractors on future complex projects to increase their likelihood of success.

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61. Quantifying Human Mobility Perturbation under the Influence of Tropical Cyclones Qi Wang (, Advisor: Dr. John E. Taylor Virginia Tech Climate change has intensified tropical cyclones, resulting in several recent catastrophic hurricanes and typhoons and making them more difficult to predict. Therefore, understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Existing research has found that human mobility can be captured by the Lévy Walk model, a bio-inspired movement model. However, we lack knowledge whether the model can predict human movements in affected areas during tropical cyclones. In this research, we attempt to quantify the influence from tropical cyclones on human mobility patterns in coastal urban populations. The research objective is to measure human mobility perturbation using high accuracy individuals’ movement data collected from Twitter and compare that with Lévy Walk model predictions. We selected three significant recent tropical cyclones which struck three coastal urban areas. These included: (1) Hurricane Sandy in New York City, (2) Typhoon Wipha in Tokyo, and (3) Typhoon Haiyan in Tacloban, Philippines. We analyzed the human mobility patterns in each city before, during and after the tropical cyclones, comparing the perturbed movement data to steady state movement data. We analyzed travel frequencies, movement distribution, duration and strength of human mobility perturbation. We discovered that while tropical cyclones changed travel frequencies and distances of urban dwellers, power law still dominates human mobility, and therefore, the animal-derived Lévy walk model is still able to describe human movement even in perturbed states. We also found that the intensification of tropical cyclones is directly related to the strength and duration of human mobility perturbation. The study deepens our understanding about the interaction between urban dwellers and civil infrastructure. It may improve our ability to predict human movements during natural disasters and help policymakers and practitioners to develop disaster evacuation and response plans.

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2014 CRC PhD Student Poster Session

62. Construction Workers’ Behavior Influenced by Social Norms: A Study of Workers’ Behavior Using Agent-Based Simulation Integrated with Empirical Methods Seungjun Ahn (, Advisor: Dr. SangHyun Lee University of Michigan Due to the labor intensive nature of construction processes, workers’ attitudes and behavior significantly affects construction project performance such as productivity and safety. Among the factors that affect worker behavior, social norms in a workgroup may act as motivational capital and play an important role in shaping workers’ behavior. However, our knowledge of the emergence and the exertion of social norms in transient construction workgroups has been very limited. With a goal to enhance our knowledge in this regard, this poster presents an interdisciplinary approach to worker behavior influenced by social norms using an integrated methodology incorporating the agent-based modeling and simulation of human behavior and empirical methods. In this approach, individual-level worker behavior influenced by both formal rules and informal rules in projects is modeled as an agent behavior rule, and a simulation unfolds the dynamic, complex systems behavior of workgroups. Empirical methods based on survey data are used to support the agent-based modeling and simulation in this research. The empirical data collected from construction workers are categorized, then compared with simulation data, and are used to create empirically supported, specific agent-based models of worker behavior. The result of this interdisciplinary effort reveals that construction workers’ behavior is indeed under the influence of social norms despite the transient nature of construction worker employment, and that workers’ self-categorization is the main mechanism of the social control in workgroups. This research also identifies the keys to the emergence of positive social norms in workgroups using simulation experiments. The findings of this research provide important implications for managing workforce in construction regarding how injunctive norms and descriptive norms can be used to manage worker behavior in construction. In addition, this work shows how empirical data collected by survey questionnaire can be used as a basis for creating empirically supported agentbased model of human behavior. Further, the applicability and extensibility of the agent-based modeling approach in construction worker behavior research is discussed in this poster.

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63. Construction site layout planning using simulation SeyedReza RazaviAlavi (, Advisor: Dr. Simaan AbouRizk University of Alberta Site layout planning is performed in the planning phase of each construction project, and has significant impacts on project safety, productivity, cost and time. This study focuses on two main tasks of construction site layout planning: identifying the size and determining the location of temporary facilities, and aims to develop a comprehensive model for estimating the size of the temporary facilities and locating them on sites in order to improve project productivity. On construction sites, size of some facilities (e.g. batch plants and equipment) is predetermined and fixed, while size of other facilities (e.g. material laydowns and stock piles) is variable and should be determined. The latter group mostly concerns facilities that temporarily contain materials. Properly estimating size of such facilities is critical, particularly on congested sites, because inaccurate estimation can result in loss of productivity and safety, and incur extra costs to projects. For sizing this kind of facility, system production rate is a significant component. However, estimating production rate is a complex process, due to interdependency of diverse planning decisions, construction uncertainties, and dynamics of construction projects. To address this complexity, a hybrid discrete-continuous simulation technique is proposed for modeling purposes. Simulation is superior in modeling construction operations and estimating system production rate by capturing uncertainties and dynamic interactions between variables. The combination of discrete and continuous simulation is used to enhance more accuracy in sizing facilities while high computational burdens are avoided. In order to efficiently use space on construction sites, possible variation of the facility size is also dynamically modeled as one of the managerial actions, which is often overlooked in the existing construction site layout planning methods. Additionally, locations of facilities influence project productivity and safety. Although many methods have been developed for optimizing facility locations, the efficiency of these methods in practice is in question. In this research, simulation is also proposed to mimic the real world of construction projects, model the interaction among influencing factors, and ultimately examine the effectiveness of different layouts. The main contribution summarized in this research is development of a holistic model enabling planning site layout along with the construction process and optimizing their corresponding variables. The proposed approach is able to integrate parameters and constraints of different disciplines including site layout, material management, logistics and construction process planning in a unified model for sizing facilities considering uncertainties and managerial actions taken to resolve space shortages. To this end, simulation is employed to sophisticatedly model the construction process and interactions between various parameters considering inherent uncertainties and managerial actions. Using an optimization engine integrated with simulation facilitates finding optimum size and location of facilities, and aids other planning decisions. The proposed approach can be applied to diverse types of construction projects such as steel structure, tunneling, earthmoving, and industrial construction, to demonstrate its adaptability and suitability.

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64. 4-Dimensional Process-Aware Site-Specific Construction Safety Planning Sooyoung Choe (, Advisor: Dr. Fernanda Leite The University of Texas at Austin Construction remains the second most hazardous industry especially due to the dangerous combination of pedestrian workers and heavy construction vehicles and machinery, such as dump trucks, dozers, and rollers. The Bureau of Labor Statistics reported that, in 2011, 15.7% of industrial fatal work injuries were in the construction industry, and 39.3% of construction industry fatalities were related to construction vehicles and machinery. Since the Occupational Safety and Health Act of 1970 was established, which places the responsibility of construction safety on the employer, various injury prevention strategies have been developed and resulted in a significant improvement of safety management in the construction industry. However, during the last decade, construction safety improvement has decelerated and, due to the dynamic nature of construction jobsites, most safety management activities have focused on safety monitoring during the construction phase. In addition, the majority of hazards are generated from specific site conditions, but current safety planning activities lack site-specific information and most safety decisions are made based on previous experience. Consequently, as projects become more complex and schedule pressure increases, potential site-specific hazards including the safety impacts of concurrent activities are not effectively identified and safety personnel cannot prepare or minimize jobsite hazards in advance. The objective of this research is to systematically formalize the construction safety planning process in a 4dimentional (4D) environment to address site-specific temporal and spatial safety information by leveraging project schedules and information technology to improve current construction safety management practices. The proposed safety planning approach includes: (1) data-driven safety database development, (2) site-specific temporal information integration (safety schedule), and (3) spatial information integration (safety 4D). In order to improve construction vehicle and machineryrelated safety, this research is focusing on horizontal construction projects which involve construction equipment-intensive activities. From the safety database, initial safety risks of project activities will be estimated and prioritized based on the combination of struck-by accident attributes and activities’ resources. The proposed safety schedule dynamically linking safety database and a project schedule will estimate the final activity risk by considering work duration and predict risky work periods. In addition, safety 4D linking the safety schedule and a project 3D model will analyze the safety impacts of concurrent activities and predict risky work zones by adding project spatial information. This safety planning approach was tested with a demo project and will be validated with a real-world elevated roadway project in future. This research will advance safety knowledge, integrating site-specific temporal and spatial information, and significantly affect the construction safety planning process. The proposed safety planning approach can provide safety personnel with a site-specific proactive safety planning tool that can be used to better manage jobsite safety by predicting activity risk, work period risk, and work zone risk in advance. In addition, visual safety materials can also aid in training workers on safety and, consequently, being able to identify site-specific hazards and respond to them effectively.

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65. The Impact of Business-Project Interface on Capital Project Performance Sungmin Yun (, Advisor: Dr. Stephen P. Mulva, Dr. William J. O’Brien University of Texas at Austin A capital project represents a significant investment by a firm to create future economic benefits. Since the global economic recession, many corporate owners have been suffering from misaligned projects and a lack of systematic approach to align project management with business strategy. Corporate owners, therefore, have paid increased attention to business-project interfaces with the aim of improving alignment between business strategy and capital project planning and execution. Despite its importance, the interfaces between business and project functions has not been adequately identified and quantitatively measured. This study intends to identify which interface exists between business and project functions throughout capital project planning and execution process, and to quantify how the business functions get involved in the process and interact with project functions. Using the quantified interfaces, this study also aims to show how the business-project interface accounts for performance outcomes in terms of cost, schedule, change, and achievement of business objectives. To achieve these objectives, this study was conducted through a correlational study based on quantitative approach. A conceptual framework was developed to measure business-project interfaces throughout capital project lifecycle in terms of the interface components: management personnel, phase, work functions, and management efforts. Based on the framework, a questionnaire was designed to identify and quantify personnel involvement and task interaction on the interfaces between business and project functions. Survey was carried out targeting industrial capital projects which have been submitted in the Construction Industry Institute (CII) performance assessment database. The performance data of the projects which responded the survey were extracted from the CII database. Data analyses were conducted through categorical data analysis methods such as Pearson Chi-square test, Somers’ d test, Fisher’s exact test, and interaction effect analysis using factorial analysis of variance. The results of the data analyses indicate that project sponsor, finance, facility/maintenance, operations/production are major business functions which are highly involved in the capital project planning and execution process. Business units interact with project units in about 60% of the work functions. Funding requests during project execution received the highest level of interaction between business and project units among all work functions. Most work functions with higher levels of interaction belonged to front end planning phases such as project scoping, capital budgeting, business objective setting, manufacturing objectives criteria setting, economic feasibility study, and technical feasibility study. In addition to that, effective business-project interface has synergy effects on performance outcomes such as block-and-tackle system. The projects with high involvement of business functions and high interaction between business and project functions have better cost, schedule, and change performance. Moreover, the business-project interface has leverage effects on performance improvement when adequate management practices are highly implemented. The projects with high involvement of business functions and high implementation of the management practices show better performance outcomes. This study provides empirical evidences for the ontological arguments of the business-project interface throughout capital project lifecycle. The study provides assessment tools to quantitatively measure the level of involvement and interaction throughout capital project planning and execution. Industry practitioners now have a quantitative assessment tool that can be used to measure the business-project interface in terms of personnel involvement and task interaction. This tool enables industry practitioners to identify and quantify the current state of the business-project interface within their organizations during the development of a capital project. In addition, the assessment tool helps them understand the interfaces by which management personnel are involved in a capital project, and which tasks require interaction between the business and project unit. The descriptive statistics from the assessment can be used as benchmarks to compare their organization’s current level to others and will be used to examine the correlation between business-project interfaces with project performance outcomes.

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66. Exploring a PREFERENTIAL Framework for Future Project Opportunities Timothy W. Gardiner (, Advisor: Dr. Yvan J. Beliveau Virginia Tech A project delivery method is defined as “a comprehensive process by which Designers (A/E), Constructors (GC), and various consultants provide services for design and construction to deliver a complete project to the Owner (O).” Design-bid-build (DBB) acts as the most common project delivery system in the United States (U.S.) today, followed by other transactional methods of construction management at risk (CM@R) and design-build (DB). The choice of delivery method has been found to have a defining impact on project results within the U.S. construction industry which still suffers from suboptimal performance including the lowest measured domestic productivity for (almost five (5)) decades. As a result, construction practitioners and academics are in endless search of alternative approaches such as Integrated Project Delivery (IPD) that might meet evolving needs and serve to counteract chronic industry challenges. In its “purest” form as a relational project delivery approach, IPD distinguishes itself from (aforementioned) transactional counterparts on a continuum of collaboration through recognizing all of the following attributes: (1) Early involvement of key participants, (2) Shared risk and reward, (3) Multi-party contract, (4) Collaborative decision-making, (5) Liability waivers, and (6) Jointly developed goals. IPD, with its trademark in 2005, has been considered an emerging delivery method with expected widespread use in the U.S. construction industry despite remaining limited in application. The research objective has been framed on the background of established “elements” found for (real estate and e-business) organizations as well as (integrated and “through-life”) project delivery approaches. With an enabler of a continuum of collaboration metric, these elements can be observed and form the basis for determining a “readiness” for transition to relational project delivery: • To evaluate the impact of “teamwork” (People); • To investigate the evolution of Information and Communication Technologies “ICT” (Technology); • To assess project life cycle “achievement” (Process); and • To critically appraise the established “language” (Legal and Commercial Structure). Current Research Question – How does each transactional project perform based on established metrics and do these (individual and collective) outcomes warrant a move to relational delivery for future work opportunities? The case study methodology has been selected to explore the potential for IPD (“contemporary phenomenon”) within a real-life context. With the organization acting as the primary unit of analysis, the case study will rely on sources of evidence from documentation and interviews of project stakeholders including O, A/E, GC as well as Specialty Contractors (SC) and Manufacturers (M). An instrument developed by literature and tested through pilot study will be employed to establish the readiness of an organization on subject projects in adopting relational principles. The findings will be verified for validity by an expert panel. Within the first decade of the 21st century, five (5) ground-up new construction projects have been completed within an approximately one hundred (100) acre business park, consisting of six (6) flex buildings and three (3) annex warehouse one-story buildings totaling 264,113 gross square feet (SF). The assemblage of five (5) projects has established the following results: • Estimated under a pre-construction arrangement and constructed under CM@R; • Designed, permitted and constructed by the same group of key stakeholders – O, A/E and GC; and • Engaged select SC and M either on multiple (more than one) or (in select instance) all (five) projects. The proposed contribution involves developing a framework for understanding the “limited in application” IPD against project delivery choices that remain prevalent today. This model identifies as a Project Readiness Evaluation Framework Emphasizing Relational Elements Named Teamwork, ICT, Achievement and Language (PREFERENTIAL) approach. Flex-type buildings offer one potential for proposed application as one of nine (9) primary property types in the domestic commercial real estate market, totaling nearly three (3) billion SF.

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67. Envisioning More Sustainable Infrastructure through Choice Architecture Tripp Shealy (, Advisor: Dr. Leidy Klotz Clemson University This project evaluates the impact of how decisions are presented, or “choice architecture,� in a prominent infrastructure-planning tool, the Envision rating system for sustainable infrastructure. The Envision system is used to evaluate, grade and reward projects for meeting sustainability criteria such as reductions in greenhouse gas emissions, preservation of wildlife habitat, and accessibility to community cultural resources. Plans to meet these sustainability criteria can receive points in five increasing levels. For example, a project earns 4-points by reducing water consumption by 25% and 17-points for 100% reduction. As currently arranged, the points in Envision reward for sustainable development plans above the industry norm default option (which receives 0-points). However, by giving points for slight improvements, Envision may unintentionally discourage the even higher levels of sustainability performance that are possible. This project explores the impact of shifting the default from industry norm, 0-points, to the level of achievement that received 17-points in the water consumption example. We draw connections from behavioral science literature, and how risk framing, reference points, status quo, defaults, and order partitions are represented in Envision. We conducted empirical studies examining the effects of default changes to the Envision point system. Undergraduate civil engineering students receive a case study depicting a brownfield site and stream restoration project in rural Alabama. They are told to complete the project design using the Envision rating system. Half of the participants receive the industry norm, 0-point, default and the other half receive the higher point default. Once the rating is complete, participants answer survey questions measuring for control variables and post-task opinions on motivation and confidence. Preliminary results indicate shifting the default to a higher level of achievement encourages users to achieve more points. Those who received the higher set default perceived the default points as greater value and losing these points is a perceived risk. Currently, we are testing default changes with a professional engineering and construction cohort. Small changes to the decision-making framework for infrastructure can impact the projects overall sustainability. We detail how choice architecture is applicable to the current version of Envision and call for more research in this area. Identifying the highest-impact decisions and their determinants at individual, organizational, and societal levels are primary research needs. This presentation opens the discussion, provides a path for future research, and details methods for using choice architecture in the current version of the Envision rating system.

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68. Segmentation and Recognition of Roadway Assets from Car-Mounted Camera Video Streams using a Scalable Non-Parametric Image Parsing Method Vahid Balali (, Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign Efficient data collection of high-quantity and low-cost roadway assets such as traffic signs, light poles, and guardrails is a critical component in the operation, maintenance, and preservation of transportation infrastructure systems. Nevertheless, current practices of roadway asset data collection are still timeconsuming, subjective, and potentially unsafe. In addition, the subjectivity and experience of the raters have an undoubted influence on the final assessments. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. For many of these roadway assets, the accurate records of locations and the most updated status are either unavailable or incomplete. Frequent reporting of up-to-date status of these assets can help practitioners in their decision makings to improve their condition and/or help avoid damages for further analysis and condition assessment purposes. To address current limitations, this research presents a non-parametric image parsing method for segmentation and recognition of roadway assets from 2D car-mounted video streams. The proposed non-parametric video-based segmentation method can easily and efficiently segment and recognize roadway assets from video streams and labels image region with their categories of roadway assets. The method can be easily scaled to thousands of video frames captured during data collection, and does not need training. Instead, it retrieves a set of most relevant video frames (e.g. highway vs. secondary road) which serve as candidates for superpixel-level annotation. Using a fast graph-based segmentation algorithm, superpixels are then obtained from each video frame and their visual characteristics are computed using a histogram of different shape, appearance, and color descriptors. Based on a Nave Bayes assumption, a likelihood ratio score is obtained for each superpixel and an asset label that maximizes the ratio is assigned. Given a video frame to be interpreted, the algorithm performs global matching against the training set, followed by superpixel-level matching and efficient Markov Random Field (MRF) optimization for incorporating neighborhood context and two types of labels: 1) semantic (e.g. guardrail) and 2) geometric (e.g. horizontal) are simultaneously assigned to the superpixels. Experimental results are presented on testing the proposed method along the U.S. Interstate I-57. The inspection vehicle can travel and collect videos at highway speeds. There are five cameras including three front view, one rear view and one down shot for pavement view that can capture images at a rate of 200 images per view per mile. The results with an average accuracy of 88.24% for recognition and 82.02% for segmentation of 8 types of asset categories reflect the promise of applicability of the proposed approach that the local visual features provide acceptable performance, while the method overall does not require any significant supervised training. The contribution of this research is the video-based parsing and segmentation methods that can leverage motion cues and temporal consistency to improve the performance of 3D roadway assets recognition. This scalable method has potential to reduce the time and effort required for developing road inventories, especially for those such as guardrails, and traffic lights that are not typically considered in 2D asset recognition methods.

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69. Integrated Computational Model in Support Of Value Engineering Yalda Ranjbaran (, Advisor: Dr. Osama Moselhi Concordia University Value Engineering (VE) is frequently applied to construction projects for better recognition of project scope and for elimination of unnecessary cost without impacting the functional requirements of individual components of constructed facilities. A critical phase in the application of value engineering is the multi-attributed evaluation of generated alternatives in the speculative phase. Cost is an essential criterion that plays an important role in the selection of the optimum or near optimum alternative that guarantees best value based on the criteria used in this process. Limited work has been carried out for automation of this process but yet without adequate visualization for the components being considered. The main objective of the current research is to propose an integrated model for building construction that provides professionals, owners and members of VE teams with automation capabilities to evaluate and compare different design alternatives of project components using multiattributed criteria as well as integrating that model with visualization capabilities to assist designers and stakeholders in making related decisions. A set of tools and techniques have been integrated in this decision making model in order to assess several alternatives and support designers/owners in making value driven selection among generated alternatives. The methodology is to develop a multi-attributed decision environment applying the Analytic Hierarchy Process (AHP) to evaluate competing alternatives. A BIM model, allowing 4D presentation of the project alternatives is implemented in the proposed model to automate data extraction for project cost estimating and to facilitate and support the visualization capabilities. The output which has been classified based on Uniformat division would be linked to the model. The model is expected to assist members of VE teams not only in costing each alternative being considered, but also in ranking competing alternative using multi-attributed criteria in a timely manner. The report can always be tracked and modified using the automated model. A prototype model that integrates the project BIM model with RSMeans cost data and AHP has been developed. Cost estimates are generated making use of direct link with RSMeans and the ranking of alternatives is performed using the Analytic Hierarchy Process. The developed model allows users to specify different evaluation criteria for each group of project components. The model has been applied to a case project to demonstrate its use and capabilities. The model evaluates and ranks generated alternatives in its output report. Large buildings projects require commitments of considerable large resources and the application of models such as that developed in this research can be of help in developing better understanding and appreciation of project scope of work and in reducing unnecessary cost without impacting the required functions of projects components being considered.

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70. Improving Campus Building Energy Efficiency and Occupants Satisfaction through Application of Artificial Intelligence into Campus Facility Management Yang Cao (, Advisor: Dr. Xinyi Song Georgia Institute of Technology With the concept of sustainability continues to grow in importance and prominence, more researches have been focused on improving energy efficiency during a building’s operation phase. However, the significant challenge is how to improve energy efficiency while considering productivity and profitability with limited resources, budget pressures and tight schedules. Academic settings, such as university facilities, also pose other unique set of problems to facility managers and administrators. Unlike commercial, residential or industrial buildings, campus facilities are composed of different building types with different requirements for indoor air quality, humidity, temperature and ventilation from continuously changing occupants. The current practice with campus facility management (FM) requires facility managers to manually schedule tasks, often based on the order of incoming requests without considering their impact on building energy consumption. To change this situation, authors conducted a series of researches on applying artificial intelligence (AI) into campus facility management. First, the proposed agent based framework can facilitate system-wide decision making for facility managers. The framework was developed to help facility managers analyze and prioritize tasks according to factors such as degree of emergency, energy impact, and occupant satisfaction level, etc. The Anylogic software with self-defined java helped to build the interactive decision framework. Moreover, AI helped to build a knowledge database for FM, which included two main parts: basic information on common daily work request and work instructions; and also the impact on building energy performance. The case based reasoning (CBR) was achieved through text mining. CBR could help facility managers to retrieve the historical cases and then get the instructions and make analysis based on past experience. It could be combined with the ABM to better analyze and prioritize future work requests based on factors such as safety, energy consumption impact, occupant satisfaction, etc. A case study was conducted on campus to validate the system with the focus on HVAC tasks. The preliminary result was the implementation of searching with simple FM text. With the foundation of these works and future endeavors, the traditional manual FM work will get much higher working productivity while improving energy efficiency and occupants’ satisfaction.

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2014 CRC PhD Student Poster Session

71. A Bio-Inspired Solution to Mitigate Urban Heat Island Effects Yilong Han (, Advisor: Dr. John E. Taylor Virginia Tech Over the last decade, rapidly growing world energy consumption is leading to supply difficulties, exhaustion of fossil energy resources, and global environmental deterioration. Contributing to these trends, 32% of total final energy consumption and nearly 40% of primary energy consumption are attributable to buildings. Urbanization is escalating these trends with tighter building spatial interrelationships. It is intensifying building energy consumption due to the mutual impact of buildings on each other and, as a result, exacerbating Urban Heat Island (UHI) effects. In this research, we attempted to seek solutions to this significant engineering issue from nature, and discovered a similar heat island effect in flowers, namely the “micro-greenhouse effect”. Although warmer intrafloral areas have evolved for botanic productive success, a cooling effect has been observed in a peculiar temperate flower—Galanthus nivalis—which generates cooler intrafloral temperatures. Our research objective is to study the special reflectance property of this flower’s shiny petals, which has been suggested as a possible contributor to this cooling effect, and develop a bioinspired reflective pattern for building envelopes. We designed a macro-level pattern with retro-reflective arrays, and conducted energy simulation of a network of buildings in EnergyPlus in eight different geographical locations. We first analyzed the temperature variations of the diffusive building surface of the control building when our proposed retro-reflective facade is applied to its neighboring surface. We then analyzed how the energy performance of the control building was affected by its surrounding network buildings with different building façades. We found that building surface temperatures dropped considerably when neighboring buildings were retrofitted with our retro-reflective façade surface. This resulted in less solar radiation being reflected from the surrounding network buildings, and therefore, mutual reflection was reduced. The results also demonstrated that total energy consumption by HVAC systems and cooling energy consumption were reduced by up to 4.4% and 6.6%, respectively, in different metropolitan areas. The study demonstrates that a bio-inspired cubic-corner-like retro-reflective façade can reduce inter-building effects, and, as a result; (1) lessen the reflected heat of solar radiation in spatiallyproximal buildings leading to reduced UHI, and (2) reduce the energy required for cooling and, therefore, energy consumption.

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2014 CRC PhD Student Poster Session

72. Forecasting Long-Term Staffing Requirements for State Transportation Agencies Ying Li (, Advisor: Dr. Timothy R. B. Taylor University of Kentucky The US transportation system is vital to the nation’s economic growth and stability, as it provides mobility for commuters while supporting US ability to compete in an increasingly competitive global economy. State Transportation Agencies across the country continue to face many challenges to repair and enhance roadway infrastructure to meet the rapid increasing transportation needs. One of these challenges is the selection of agency staff. With a large number of employees retiring from the transportation workforce and deceasing interest in a career in transportation engineering among young people, State Transportation Agencies have to make every effort to attract potential employees. Various workforce development programs are designed to prepare staffing pool for 10-15 years into the future. In order to effectively plan for future staffing levels, State Transportation Agencies need a method for forecasting long term staffing requirements. However, current methods in use cannot function without well-defined projects and therefore making long term forecasts is difficult. The current work seeks to answer the question: How do factors like transportation system demand, current transportation system performance, funding, and staffing strategies impact staffing level requirements at State Transportation Agencies? To be more specific, the current work seeks to identify: 1) what feedback structures link future transportation system demand, current system performance, funding, staffing strategy and future staffing level requirements; 2) what are the main drivers and constraints that determine future staffing levels and how these drivers and constraints impact State Transportation Agencies’ staffing strategies; 3) how strategy developers can effectively address potential staffing shortages and overflows in State Transportation Agencies. System Dynamics modeling methodology will be used to conduct the current research. The researchers are collecting data through literature review, survey and interviews with State Transportation Agency personnel to identify how various factors impact long term staffing needs. Data collected will be used to construct a system dynamics model which maps out causal links and feedback structures within the system. Once the model is empirically tested and validated, the model will be able to predict long term trends of future staffing needs as well as run simulations to reflect different staffing strategies. The authors are currently in the process of constructing the model. Some preliminary findings include: (1) State Transportation Agencies are managing larger roadway systems with fewer in-house staff than they were 10 years ago; (2) Outsourcing is becoming more common mainly due to limited availability of qualified in-house personnel; (3) At this moment, the adoption of mobile information technology within State Transportation Agencies appears to be limited and the impact of those technologies on staff efficiency is also limited. The system dynamics model being developed for the proposed research will hopefully fill in the blank of long term forecasting method for transportation workforce needs. The model will provide insights on long term trends and fluctuations in transportation workforce needs. State Transportation Agencies may benefit from the proposed research by gaining knowledge about the system that impact staffing requirements in order to effectively develop staffing strategies. Researchers and engineers in other disciplines may modify the model to forecast work volumes and staffing needs for other industries.

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2014 CRC PhD Student Poster Session

73. Vision-based Building Energy Diagnostics and Retrofit Analysis using 3D Thermography and BIM Youngjib Ham (, Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign Accurate quantification of the energy cost savings associated with retrofitting performance problems in existing buildings can minimize the financial risks in retrofit investments. Nevertheless, the industry continues to face several technical challenges in identifying potential areas in building envelopes for retrofit and providing recommendations based on sound cost-benefit analysis of the retrofit alternatives. To address the current needs in energy diagnostics and retrofit decision-makings, infrared thermography and BIM-based energy analysis tools (e.g. EnergyPlus) are being widely used. However, current practices of manually interpreting large amounts of visual thermal data and leveraging as-designed building conditions declared in industry standard databases in the current BIM-authoring tools often lead to subjective and inaccurate assessments. For existing buildings, without considering the diminishing thermal properties of building elements caused by deteriorations and updating the associated material properties in BIM, the results from BIM-based energy analysis will not be trustworthy. This research aims to create and validate an easy-to-use tool and automated methods based on 3D thermography and BIM to support reliable cost-benefit analysis of building energy efficiency retrofits and improve the reliability of BIM-based building energy performance analysis. First, by using a consumer-level hand-held thermal camera, practitioners collect large numbers of unordered thermal images from building environments under inspection. Then, using a new computer vision based algorithm, 3D thermal models are generated wherein actual surface temperatures are modeled at the level of 3D points. 1) Building areas with potential thermal deteriorations are detected by comparing the actual measurements with the energy performance benchmark resulting from a numerical analysis. By using 3D thermal distribution and environmental assumptions that the indoor heat transfer is attributed to thermal convection and radiation under a quasi-steady-state condition, actual thermal resistances (R-value) are calculated at the level of 3D points. Then, based on the ‘degree days’ data, we estimate energy saving costs when thermal resistance of defective areas are increased to their recommended level. 2) We automatically map the actual thermal resistances at the level of 3D points to their corresponding BIM elements in gbXML schema. This is done by discretizing building elements in BIM into a mesh and using the nearest neighbor searching algorithm. We then derive a single actual R-value for each building element, and automatically update the corresponding entry for the thermal resistance in the as-designed BIM. The outcome can be used as an input of BIM-based energy analysis tools for more accurate analysis. We have conducted several experiments on two real-world residential and instructional buildings in Virginia and four hypothetical cases in Minnesota and Florida. Our findings on the difference between the actual thermal resistance measurements and the notional values declared in standard methods such as ISO 6946 were about 10%. Our experimental results for cost-benefit analysis show that the proposed method can reliably estimate ROI associated with retrofitting thermal performance problems and has potential to improve today’s practices of financial feasibility analysis on building retrofits. Also, by shortening the existing gaps in knowledge about energy performance modeling between the architectural information in the as-designed BIM and the actual building conditions, this research enables reliable BIM-based energy performance analysis. The primary scientific contributions are as follows: 1) an automated method for comparing actual and expected building energy performance in 3D and analyzing the deviations to detect potential performance problems; 2) a method for measuring actual thermal resistances of building assemblies in 3D; 3) a method for automated association of actual thermal property measurements to building elements in BIM; and 4) an automated method for updating their corresponding thermal properties in gbXML schema of BIM. Over the next 30 years, about 150 billion S.F. (roughly half of the U.S. building stock) will require retrofit to meet the new rigorous energy standards. Non-compliance with the new energy standards is not limited to existing buildings. About a quarter of the newly constructed and certified buildings do not also save as much energy as their designs had originally predicted. Construction companies can leverage the findings of this research and the developed tools to create new workflows for building commissioning – particularly for LEED certified buildings– and also new workflows for energy efficiency retrofit assessment processes.

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2014 CRC PhD Student Poster Session

74. Multi-Tiered Selection of Project Delivery Systems for Capital Projects Zorana Popić (, Advisor: Dr. Osama Moselhi Concordia University In this research, a decision support system (DSS) for selecting most suitable project delivery systems (PDSs) for capital projects is proposed. Project delivery systems continue to evolve, to meet challenging project objectives. Selecting a PDS is an early project decision, which can greatly affect the project execution process and its outcomes. Existing methods for PDS selection do not consider all the important recent developments in project delivery, including Integrated Project Delivery (IPD) and Public-private Partnership (PPP), within a single comprehensive decision support system. The objective of this research is to develop a decision support system for selecting the most suitable project delivery systems for capital projects, which ranks the available PDS alternatives in order of suitability. Research method includes an in-depth analysis of 15 case studies of projects constructed in the USA and 207 projects in Canada, which utilized PPP delivery methods. The selection criteria were developed utilizing related literature and the findings of the analysis of the case studies. As a result, the proposed DSS encompasses a multi-tiered process. It operates in two distinct modes; elimination, first, to narrow the search field, and ranking, second, to find the most suitable delivery method. In the first mode, the suitability of PPP is identified and a number of PDSs are eliminated based on a set of key project characteristics. In the second mode, evaluation and ranking of the remaining PDSs are performed using multi-attributed decision method. The decision maker provides project-specific inputs including project and owner characteristics, and judgments regarding the importance of specific evaluation and selection criteria. The multi-attributed decision method (MADM) model utilizes relative effectiveness values (REVs) of PDSs in the evaluation process. These values build upon those developed by the Construction Industry Institute (CII, 2003) to account for PDSs and selection factors beyond those considered in the CII study. Three case projects were analyzed using the proposed DSS, including one private sector project and two public sector projects. In two of the three cases, the selected PDS was recently developed integrated project delivery (IPD). The principal contributions of this research consist in providing a decision method which introduces and makes available newly developed PDSs, including IPD and the family of PPPs, as well as the criteria for PDS selection which take into account current tendencies in the construction industry. The proposed DSS defines specific screening criteria, to eliminate from further consideration non-applicable or impractical alternatives, so that detailed evaluation can be concentrated on the most relevant alternatives. Hierarchy and network structures for application of analytical hierarchy process (AHP) and analytical network process (ANP) have been developed, which incorporate 67 factors for selecting among non-PPP alternatives and 61 factors for selecting among PPP alternatives. Preliminary relative effectiveness values for 16 non-PPP alternatives with respect to the 67 selection factors are proposed. An automated software tool was developed to facilitate the use of proposed DSS. The proposed DSS is intended for decision makers of owner organizations, and their consultants, who seek a rational, knowledge-based approach to PDS selection decision.

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2014crc postersessionproceedings