Automation and Affordability: The Impact of Automation in the Affordable Housing Construction

Automation and Affordability The Impact of Automation in the Affordable Housing Construction

Dissertation Report Laura KovaÄ?ević 9th October 2020 Copenhagen School of Design and Technology Class 7J

Mentors Mille Wilken Bengtsson Alexander Matthias Jacobson

Abstract Rapid global growth of urban population urgently requires the expansion of urban environment and infrastructure. Common issue of housing shortage caused by the increasing gap between housing supply and demand results in decreasing affordability and liveability in metropolitan areas (Alvarez, et al., 2017). Yet, the productivity of the conventional construction industry has been declining for the past decades. Contemporary construction techniques are slow and mostly unable to accommodate the universal housing needs. On the other hand, automated construction techniques have a potential to change the existing paradigm and encounter large scale adoption due to the benefits of utilizing innovative technologies (Bock, 2015; Delgado, et al., 2019; Keating, et al., 2017). Despite the prospective advantages, the implementation of automated construction techniques is still limited by numerous factors (Delgado, et al., 2019).

The paper aims to investigate the impact of automation in affordable housing construction.

“Contextualizing Housing Affordability” serves as a theoretical base for familiarization with the concepts of price per income ratio, housing consumption, liveability and housing shortage. “Affordable Housing Construction” is analysed as a theoretical and technical framework that questions conventional construction techniques, housing procurement, design approach and management methodology.

“Automated Construction” is observed through a theoretical scope of challenges that limit the implementation of automated construction techniques. Eventually, “Presentation of Research” aims to test the mentioned limitations in the form of case study. The result is an in-depth insight into the industry-specific practical solutions, comparable figures and implications for further development.

Study is focused on the principal research question: Can robotic automated technology contribute towards the cost reduction in the large-scale affordable housing construction?

In addition,

supplementary research questions are investigated: What are the factors causing the stagnation in the current construction industry? What are the factors limiting the adoption of robotics in the construction industry?

The paper finds that significant construction time saving achieved by the automated technologies seem to be the key factor for lowering the cost of affordable housing construction. Notable reduction of time in the housing supply chain is not feasible by using conventional construction techniques. On the contrary, automated construction techniques provide prospective solutions regarding time saving in the housing supply chain.

Content of the study is subdivided into 7 parts. Initial service parts (1) are followed by Introduction (2) which gives an overview of the framework. Then, Method (3) provides a description of methodology.

Theoretical base (4) of the study is subdivided into three chapters: Contextualizing Housing

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Affordability, Affordable Housing Construction and Automated Construction. Analysis (5) is presented in two chapters: Presentation of Research and Case Study Through the Factors. Lastly, Conclusion (6) is followed by closing service parts (7).

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“I hereby confirm that I have carried out this dissertation report without any unrightfully help.” (referring to Order no. 714 of 27 June 2012, chapter 5, § 18, section 6).

Laura Kovačević

9th October 2020 Copenhagen, Denmark

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Table of Contents Abstract ................................................................................................................................................... 1 Table of Figures ...................................................................................................................................... 6 Introduction ............................................................................................................................................. 7 1.1 Background Information ............................................................................................................... 7 1.2 Context Relevance ........................................................................................................................ 7 1.3 Motivation ..................................................................................................................................... 8 1.4 Research Purpose .......................................................................................................................... 8 1.5 Target Group ................................................................................................................................. 8 1.6 Limitation...................................................................................................................................... 8 Method .................................................................................................................................................. 10 2.1 Outline Methodology .................................................................................................................. 10 2.2 Literature Review........................................................................................................................ 10 2.3 Source Validation........................................................................................................................ 10 2.4 Comparative Analysis ................................................................................................................. 10 2.5 Case Study .................................................................................................................................. 11 Contextualizing Housing Affordability ................................................................................................ 12 3.1 About .......................................................................................................................................... 12 3.2 Price per Income Ratio................................................................................................................ 12 3.3 Housing Consumption ................................................................................................................ 13 3.4 Liveability ................................................................................................................................... 14

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3.5 Housing Shortage ........................................................................................................................ 14 Affordable Housing Construction ......................................................................................................... 16 4.1 Stagnation of the Conventional Construction Industry ............................................................... 16 4.2 Economic Drivers in the Housing Procurement.......................................................................... 16 4.3 Paradigm Shift ............................................................................................................................ 18 Automated Construction ....................................................................................................................... 21 5.1 About .......................................................................................................................................... 21 5.2 Challenges of Adoption of Automated Systems in Construction ............................................... 21 5.3 Contractor-side economic factors ............................................................................................... 22 5.4 Client-side economic factors ....................................................................................................... 23 5.5 Technical and work-culture factors............................................................................................. 23 5.6 Weak business case ..................................................................................................................... 25 Presentation of Research ....................................................................................................................... 26 6.1 About .......................................................................................................................................... 26 Case study through the factors .............................................................................................................. 28 7.1 Contractor-side economic factors ............................................................................................... 28 7.2 Client-side economic factors ....................................................................................................... 30 7.3 Technical and work-culture factors............................................................................................. 31 7.4 Weak business case ..................................................................................................................... 37 Conclusion ............................................................................................................................................ 42 Bibliography ......................................................................................................................................... 44

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Table of Figures Figure 1 Europe: Property Prices Index by City 2019 (based on Numbeo.com, 2019) Figure 2 Financial housing taxonomy (based on Alvarez, et al., 2017) Figure 3 Relation of pivotal factors in the housing model (based on Alvarez, et al., 2017) Figure 4 Procurement models (based on Open Systems Lab, 2018) Figure 5 Factors limiting the adoption of robotics in the construction industry (based on Delgado, et al., 2019) Figure 6 Comparison of existing automated construction research (based on Keating, et al., 2017) Figure 7 Proof-of-concept 3D printed house in Austin (photo by R. Morton; source: ICON, 2020) Figure 8 Vulcan II 3D printer (source: ICON, 2020) Figure 9 Vulcan 3D printer extruding Lavacrete (photo by R. Morton; source: ICON, 2020) Figure 10 Tabasco (Mexico) before the project development (photo by J. Gonzales; source: New Story, 2020) Figure 11 Vulcan 3D printer (photo by J. Perez; source: New Story, 2020) Figure 12 3D printed unit in Tabasco (Mexico) (photo by J. Perez; source: New Story, 2020) Figure 13 Creating six additional units for Community First! Village in Austin by using 3D printer Vulcan (photo by R. Morton; source: ICON, 2020) Figure 14 Six 3D printed units upon completion as a part of Community First! Village in Austin (photo by R. Morton; source: ICON, 2020) Figure 15 Single-family homes sales price breakdown (based on Ford, 2020) Figure 16 Single-family house price and cost breakdown (based on Ford, 2020)

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Introduction 1.1 Background Information Dissertation is carried out as a final Bachelor project of Architectural Technology and Construction Management course at Copenhagen School of Design and Technology. Investigation of innovative utilization of emerging technologies within the construction industry has a possibility to shape the future framework and enhance living standards for many. While the conventional construction methods are prone to stagnation and automated ones to limitation, housing shortage and unaffordability will keep on increasing. By understanding the underlying challenges that the current industry is facing, I aim to provide an outline for insight and further implications.

1.2 Context Relevance General statistics indicate that the world is heading towards 70% urbanisation by 2050. In 2015, Europe, South and North America accounted for 73%, 83% and 82% of people respectively living in cities, towns and other urban settlements. Meanwhile, Africa and Asia note 40% and 48%. Despite uneven distribution of urbanisation across the globe, numbers indicate that more than a half of the world’s population is urban (Burdett, 2015). “The urban population of the world has grown rapidly from 751 million in 1950 to 4.2 billion in 2018� (United Nations, 2018). However, certain exponential growth comes with destructive consequences. Cities are expected to be principal sites for generating urban poverty by 2035 rather than rural areas (Fleetwood & Meija, 2012). Increased unaffordability will greatly impact the quality of housing and liveability. Studies suggest that regulatory housing supply constraints highly affect the increased price of housing. Despite some affordability problems being linked to poverty, they are mostly caused by human-made constraints on the supply of land and floor space (Bertaud, 2018). While the numerous housing policies and governmental programs are determined to address the rising housing shortage and unaffordability issues, the construction industry is not effective in following the growing demand for a built environment. For decades, the conventional construction industry is plagued with stagnation due to slow, labour-intensive, dangerous and expensive techniques (Bock, 2015; Delgado, et al., 2019; Keating, et al., 2017). Yet, the construction industry universally has a huge economic importance. The spending in the construction sector tends to contribute as high as 9%-15% of GDP in most countries. Robotics and automated systems could provide numerous benefits in overcoming these challenges within the overall Architecture, Engineering and Construction (AEC) industry. Despite the impressive comparison of numerical figures that go in favour of advanced automation and robotic systems, implementation in the current construction industry seems to be challenged by numerous factors (Delgado, et al., 2019).

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1.3 Motivation Selected experiences in the past academic years have shaped my professional interests towards the two topics that I am combining within this dissertation. Exchange semester at Politecnico di Milano (September 2019 – January 2020) has been vastly informative on the questions and concepts of affordable housing in contemporary urban development.

On the other hand, a semester internship at Bjarke Ingels Group provided an insight into the fields of automation and robotics. I am noticing a great potential of automation within the AEC industry due to numerous demonstrated benefits. Novel professional contribution can be done in the construction, management and design phase of housing supply which I aim to promote with this dissertation.

My motivation lays within the analysis of the innovative techniques and their utilization in order to impact the construction phase in the current housing procurement models. Furthermore, by addressing crucial questions of future urban and technological development, I do hope to spark meaningful dialogues within the industry considering the emerging opportunities.

1.4 Research Purpose The objective of the research is to consider the possibilities of implementation of automated systems within the stagnating construction industry in order to impact the cost of affordable housing construction. Study is focused on the principal research question: Can robotic automated technology contribute towards the cost reduction in the large-scale affordable housing construction? In addition, supplementary research questions are investigated: What are the factors causing the stagnation in the current construction industry? What are the factors limiting the adoption of robotics in the construction industry?

1.5 Target Group Primary target groups are young professionals interested in innovation within the construction industry in the connection to the provision of affordable housing. Analytical approach of this dissertation aims to serve as an inspiration while considering and implementing new working methods in order to foster innovative solutions within the AEC industry.

1.6 Limitation Due to the limited practical uses of the innovative technologies within the global AEC industry, the scope of this dissertation will be limited by geopolitical factors and regional differences. The selected theories and data on affordable housing will be based within the European and US frames. However,

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the references and examples may consider the global trends in the affordable housing market in order to foster learning from various cases. Furthermore, dissertation will address a few implications for the roles of design and management in the housing model but focus on construction. The aim is to demonstrate the impact of design and management on the construction process. Next, dissertation is not specifically focused on a certain type of automation and robotic technology for construction. However, in order to limit case study research, additive manufacturing is selected and explored in depth. Finally, case study examines the construction technologies company based on the performance in the relation to the theoretical factors. Performance will not be examined from the economic and business standpoint but rather architectural, technological and technical.

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Method 2.1 Outline Methodology The following section gives an insight into the research approach and general dissertation outline.

Validation of investigated qualitative and quantitative data forms a structured theoretical base for the research of the principle case study which aims to answer the research questions.

2.2 Literature Review Reviewed literature is categorized based on the two principal subjects – Affordability and Automation.

First, contextualization of the “Housing Affordability” is based on the theories, studies and charts from peer-reviewed and scholarly literature. Second, lessening the scope of “Housing Affordability” allows concentrated research towards “Affordable Housing Construction”, supported by theories, charts and case studies from peer-reviewed, scholarly and editorially reviewed literature. Third, “Automated Construction” explains the concepts of technological implementation and focuses on construction industry-specific challenges for adoption of automated systems based on the peer-reviewed scientific article from academic literature.

2.3 Source Validation Literature review is accompanied by source validation in the form of CRAAP test. According to Kurpiel (2020), “CRAAP is an acronym for Currency, Relevance, Authority, Accuracy, and Purpose”. All collected literature was examined and grouped in accordance with the listed categories. “Currency” examines the timeliness of the information. “Relevance” concerns the importance of the information in the relation to the research questions. “Authority” questions the source of the information and “Purpose” investigates the reason for including information into the scope of writing (ibid.). In addition to the CRAAP test, sources were evaluated based on the subject relevance for the proposed research questions.

2.4 Comparative Analysis General overview of the current state in the automated construction industry was conducted in the form of comparative analysis of the most notable manufacturers of 3D printed housing on the market. In order to identify the leading manufacturers, editorially reviewed sources and consultancy reports have been investigated (Goehrke, 2020; Research and Markets, 2019). Manufacturers were selected based on the following four conditions: 1. Automated system used in the construction must be additive manufacturing.

2. Specialization must include affordable housing construction.

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3. There must be at least one built reference project.

4. Regional focus preferably to be within Europe or the United States.

Subsequently, the range was reduced to the selected six manufacturers. In order to specify one for the case study of the dissertation, three categories were investigated: the variety and abundance of the accessible data, prospective business case within the construction industry and internal technological progress. Afterwards, the most suitable one is chosen based on the compliance with the research questions.

2.5 Case Study The core of the case study is examination of the selected manufacturer on the base theory by Delgado, et al. (2019) “Factors limiting the adoption of robotics in the construction industry�. Base theory consists of four categories and 11 factors which were observed in the relation to the performance and context of the case study.

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Contextualizing Housing Affordability 3.1 About Current global trends indicate a significant growth of urban population and urban environment. Studies from 2009 show that 90% of the world’s urban growth occurs in the developing countries, about 70 million residents a year. By 2035, the cities are expected to become uppermost sites of poverty rather than rural areas. Notable forecasts also suggest that 7 out of 10 people will be urban inhabitants by 2050. Taking that into the account, there will be an extensive need for expansion of the built environment and civic infrastructure. Due to the population growth, urbanization and economic development, 60% more energy will be consumed by 2030 (Fleetwood & Meija, 2012). The gap between an extreme demand and inadequate supply of housing results in the increasing unaffordability of the contemporary cities which disrupts overall liveability (Alvarez, et al., 2017).

3.2 Price per Income Ratio Similar to the expanding global trends, the majority of the European capitals are considered unaffordable for its residents. This chapter demonstrates the alarming situation within developed economies of European capitals on the issue of affordable housing. There are various methods of measuring affordability. In general, the city is considered unaffordable if one pays more than 30% of disposable income for housing (Bertaud, 2018). In order to compare cities by using the simple affordability index, the price / income ratio (PIR) provides an insight into the current trends. PIR measures housing affordability in a city by comparing the median price of a dwelling with the median household income. Nevertheless, PIR does not indicate the location or how much housing a household gets for the median price. PIR also excludes rents and applies only to sales. However, it is still used due to the simplicity of comparing the price of housing in different cities in relation to the different income levels. PIR values are categorised as follows: affordable (<=3), moderately affordable (3.1 to 4.0), seriously unaffordable (4.1 to 5.0) and severely unaffordable (>=5.1) (ibid.). In order to understand the current context within European frames, 103 cities were ranked based on PIR in 2019 (Figure 1) (Numbeo.com, 2019).

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Europe: Property Prices Index by City 2019

Belgrade London Rome Paris Moscow Split Lisbon Prague Milan Tirana Valencia Plovdiv Tampere Katowice Brussels Malmo Rotterdam Reykjavik Antwerp Eindhoven

0

5

10

15

20

25 Price to Income Ratio

Figure 1 Europe: Property Prices Index by City 2019 (based on Numbeo.com, 2019)

On the top of the list are Belgrade (22.21), London (20.83), Rome (19.80), Paris (19.12) and Moscow (18.41). Meanwhile, at the bottom of the list are Malmo (6.76), Rotterdam (6.69), Reykjavik (6.45), Antwerp (6.01) and Eindhoven (4.48). Presented formula assumes and uses net disposable family income, 90 square meters as a size for a median apartment and the price of a square meter as the average price of square meter in the city centre and outside of the city centre (ibid.). Given data indicates that 102 examined cities had PIR above 5 which makes them severely unaffordable for its citizens. Certainly, causes of high/low affordability of each capital greatly depends on the historical, political, economic, cultural etc. background. The cost of variables that compose construction, such as labour, materials and framing, vary in different markets. Accordingly, automation within the construction industry offers different value in each market.

3.3 Housing Consumption Affordability is tightly linked to Housing Consumption which, when governmentally regulated, might unintentionally cause severe damage for the lower-income groups. Affordability issues can potentially be solved with governmental determination and a long term-vision (Fleetwood & Meija, 2012).

Unfortunately, there is no one-size-fits-all solution due to the individual complexities of various urban

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structures (Bertaud, 2018). However, there is a common pattern in the approaches. “Affordable housing policy aims to increase low-income households’ housing consumption until they have reached a socially acceptable level“ (ibid.). Housing consumption can be measured in many ways: floor space per household, land area per household, residential utility consumption etc. Governments regularly set minimum consumption standards for housing as an initial step in establishing housing policies.

However, studies are showing that even the well-intended initiatives result in the bad outcomes for underprivileged citizens. Essentially, the households with the housing consumption lower than the regulated minimum standard become a part of the informal sector where access to public services is limited or non-existent. Therefore, cities can improve housing standards for the underprivileged socioeconomic groups by legitimizing informal settlements and loosening up the regulations for minimum housing consumption (ibid.). Nonetheless, the current situation is alarming since 1 out of every 3 city dwellers (around 1 billion people) live in slums. The trend is expected to grow over the next 10 years (Fleetwood & Meija, 2012).

3.4 Liveability Liveability, defined as a quality of life within a certain area, strongly relates to affordability.

Exponential growth of urban dwellers indicates that households eventually adjust to unaffordable PIR. That has a direct influence on liveability of the city by lowering the life quality of its residents (Alvarez, et al., 2017). It can be manifested in consuming insufficient housing (e.g. in quality, price/distance from the labour market etc.) due to the unavailability to afford the high costs of land and construction. Floor area, location and price per square meter are fundamental components defining the user’s choice of housing. Housing policies tend to focus on design improvements and area increment since the quality of construction and size are the most noticeable attributes. However, ignorance of the location factor might cause the failure of the well-intended initiatives. In order to establish appropriate housing policies and provide habitable social housing, all attributes must be addressed (Bertaud, 2018). Again, automation within the construction sector has a possibility to target different market-specific challenges.

Floor area and price per square meter can be addressed by the automated solutions within the housing supply chain, while it is not the case for location factor.

3.5 Housing Shortage When contextualizing affordability, there is a need to introduce the occurring housing shortage.

Logically, prices increase when the demand is higher than the supply. As a result, affordability is severely impacted. When it comes to the quantitative data on housing demand, it is calculated that 96,150 new affordable units every day (or 4,000 every hour) need to be built over the next 25 years in order to accommodate the world’s urban housing needs (Fleetwood & Meija, 2012). That is a serious challenge for the construction industry. Undoubtedly, the resources will be allocated towards the 14

construction market. Three countries that account for over a third of the worldâ&#x20AC;&#x2122;s population and economic output, China, US and India, will account for 57% of all global growth in the construction and engineering market by 2030. Moreover, the estimation is that by 2030 construction will account for 14.7% of global GDP, up from 12.4% in 2014. Even though there is no data addressing affordable construction, the finding suggests that nations must find alternative ways to fund infrastructure, other than by public funding (Global Construction Perspectives and Oxford Economics, 2015).

In order to protect the underprivileged citizens, it is important to consider the broader context of the housing shortage. Not only low-income class is affected by the housing shortage, but also the middle class. Decreasing liveability and housing shortage puts middle class citizens in the position to seek for available housing stock of the low-income class. For example, during the 1970s in Chennai (India), the government was forcefully providing public housing for low-income groups, while limiting construction and land development for all other income groups through bureaucratic regulations.

Despite governmental control, the low-income groups were either subletting or informally selling housing units to the upper-income groups. Consequently, the low-income groups have eventually moved back to the slums due to the economic factor. Other examples within the urban environment include the occurrence of gentrification which causes displacement of the underprivileged socioeconomic class among other consequences. Therefore, planners and housing policies should consider the presence of housing shortage while providing social housing for low-income groups (Bertaud, 2018).

Seemingly, the greatest challenge of delivering needed housing is the time variable. The significant reduction of time in the housing supply chain is not feasible by using conventional construction techniques. On the contrary, automated construction techniques provide prospective solutions regarding time saving in the housing supply chain. Design and management, preceding and tightly linked to construction, also have a possibility to experience time savings by implementing automated solutions.

Following chapters will provide more information on this matter.

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Affordable Housing Construction 4.1 Stagnation of the Conventional Construction Industry Considering the forecasts described above, it is clear that the existing paradigm of housing supply is not adequate and must be altered towards emerging technological and digital development. Experts predict that technological development will foster supply-side amelioration with the significant increase of efficiency and productivity (European Commission, 2015; Schwab, 2016). Furthermore, new global markets, cost reductions in the terms of transportation, logistics and trade will result in the overall economic growth (Schwab, 2016).

In order to present the requirements for paradigm shift, the current state of the industry must be introduced and understood. From a historical perspective, every milestone in industrial development is characterized by revolution. Revolution emerges in the form of impactful innovation and modernization. The First Industrial Revolution began in the late 18th century in Britain. It is characterized by mass-production and machine-based manufacturing. Afterwards, the Second Industrial Revolution in the late 19th and early 20th century utilized electric power for the purpose of mass production. The Third Industrial Revolution (also called the Digital Revolution) started in the 1950s and it has automated production by the use of electronics and information technology (Tkaczyk, 2019).

Currently, the signs of a new technological era are appearing. Fourth Industrial Revolution sets foundations in Digital Revolution but differs because â&#x20AC;&#x153;it is characterised by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheresâ&#x20AC;? (Schwab, 2016).

Distinctive feature of the Fourth Industrial Revolution is implementation of emerging technologies, such as artificial intelligence, robotics, the Internet of Things, additive manufacturing etc. It is evolving at the exponential rather than linear pace due to the unforeseen velocity and broad reaching scope (ibid.).

Meanwhile, the construction seems to be unable to cope with rising product complexity. Conventional construction industry faces stagnation due to the declining productivity, rising defect rate, growing organizational problems and cost overruns (Bock, 2015). The studies based on analysis of current trends suggest that emerging technologies will outperform the conventional ones in the next 10-20 years (Bock, 2015; European Commission, 2015).

4.2 Economic Drivers in the Housing Procurement Analysis of current housing procurement models on the market allows identification and categorization of the costs. Presented housing procurement models (Figure 2) were taken from various housing markets, spanning from Germany to Australia. Those are Speculative Developer, Nightingale, SelfProcure, Customised (Architect led), CLT Self Provide, CLT Coop, Self-Build and Social Housing

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(rental). Each model varies in the share of ownership, finance and construction. Graphical comparison allows indication of the costs composing each model. Since the studies were referring to the Danish housing market, the price per square meter is expressed in DKK on the vertical axis (Alvarez, et al., 2017).

Financial housing taxonomy

100,000 maintenance user mortgage 90,000

VAT profit developer interest loan

80,000

management + design construction 70,000

land

60,000

50,000

40,000

30,000

20,000

10,000

Speculative Developer

Nightingale Self Procure Customised

Figure 2 Financial housing taxonomy (based on Alvarez, et al., 2017)

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CLT Self Provide

CLT Coop

Self build

Social Housing

Figure 2 shows that certain costs can be reduced or eliminated by pursuing different models. However, the factors of land, construction and maintenance are present in every model which makes them pivotal.

It is important for the context of this dissertation to emphasise the presence of management and design factor since it is appearing in every model except the Self build model. Also, in order to understand the accustomed housing supply chain, management and design factor must be taken into the account. As demonstrated, out of listed pivotal factors (including management and design factor), construction seems to be the most expensive one, followed by maintenance. Therefore, it should be highlighted and examined in the terms of affordability. Although construction ranks the highest out of all factors, it is proved that the cost is immensely impacted by preceding design and management factors in the process (Figure 3) (Alvarez, et al., 2017; Open Systems Lab, 2018).

Relation of pivotal factors in the housing model

LAND

DESIGN

MANAGEMENT

CONSTRUCTION

HOUSE

MAINTENANCE

Figure 3 Relation of pivotal factors in the housing model (based on Alvarez, et al., 2017)

In other words, design principles and project management execution will define the overall character of the construction. Moreover, the quality of construction directly affects the housing maintenance cost for the end users. Low quality construction results in higher household resource consumption and maintenance (Bertaud, 2018). Consequently, in order to reduce the cost of construction, the existing paradigm of preceding actions must be reconsidered, together with the construction itself (Open Systems Lab, 2018; Alvarez, et al., 2017).

4.3 Paradigm Shift Present-day design and management methods must be reconsidered due to direct impact on the construction phase. In order to enhance productivity within the construction industry, preceding design and management methods have to provide an adequate base. This chapter introduces exemplary design proposals and discusses advanced management workflows.

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Optimization of design workflows is crucial as technologies evolve in order to utilize the technological possibilities to the full extent and meet construction delivery expectations (Bernstein, 2018). Experts argue that the work of architects must become more specific, information-driven and quantitative based (Bernstein, 2018; Bock, 2015; European Commission, 2015; Open Systems Lab, 2018). Rapid development of integrated design software had a significant impact on the architectural competences.

Such competencies include programming, familiarity of fabrication techniques and material engineering. Therefore, architects are expected to consider technological capabilities of the available machinery in the design development process (Tkaczyk, 2019).

As a response to the dynamic pace of technological advancements, numerous design theories are being proposed. For example, Bock (2015) investigates Robot-oriented design (ROD) which was first introduced in 1988 in Japan. ROD was developed for supplementing the conventional construction sector and improving component design to the standards of the latest technological inventions. ROD enhances the simplicity and applicability of automation or robotic technology by co-adaptation of construction products, processes, organization and management (ibid.).

Furthermore, Open System Lab (2018) promotes Design for Manufacture and Assembly (DfMA). The main principle of DfMA is based on breaking the product into modular components in order to allow complex, standardized and precise assembly work done by machines and assembly teams. In that way quality control and efficiency are enhanced since the work is broken down into the stages and done in advance in factory conditions. In the building example, Open System Lab (2018) uses categorization of 7 systems or layers of a house based on the concepts by Frank Duffy and Stewart Brand: site, structure, skin, services, seal, space plan and stuff. Treating these systems as independent as possible from each other allows various design options within the DfMA language. In other words, â&#x20AC;&#x153;the idea of DfMA is to apply innovation and design thinking to optimise for all stages of the productâ&#x20AC;&#x2122;s life, especially the production processâ&#x20AC;? (ibid.). Furthermore, the concept of DfMA is based on Design for Manufacture and Design for Assembly and can be extended to Design for Maintenance and Design for Disassembly.

Design for Manufacture aims for minimised cost, time and material consumption in the manufacture process while optimising factory set-up cost, efficiency and precision. Meanwhile, Design for Assembly aims towards lowering the factors of time, cost, skill and complexity during assembly. The method behind this design approach is a production of independent standardized components that can be simply assembled without requiring competent construction skills. Furthermore, Design for Maintenance considers reduction of cost and difficulty of maintaining, repairing and replacing components during the life cycle of the building. Finally, Design for Disassembly aims to design reusable components of the building for the eventual simple and safe process of disassembly (ibid.).

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Additional example of a design proposal is Material-based Design Computation investigated by Neri Oxman (2010). It advocates for material aware design “as a set of computational strategies supporting the integration of form, material and structure by incorporating physical form-finding strategies with digital analysis and fabrication” (Oxman, 2010). Heterogeneous design thinking usually distinguishes modelling, analysis and fabrication as independent disciplines. It also initiates with development of form instead of prioritizing environmental performance and material behaviour. The frequent result of the addressed design process is waste due to the disintegration of the crucial segments (ibid.).

Consequently, The Digital Construction Platform (DCP) has been examined in order to put the proposed Material-based Design Computation concept in practice. The DCP system “consists of combined hydraulic and electric robotic arms in a micro-macro manipulator configuration, mounted on a tracked mobile base” (Keating, et al., 2017). The DCP system has constructed a 14.6-m-diameter, 3.7-m-tall hemispherical open dome using 3D print-in-place additive fabrication (ibid.). Given the above, technical examination of automated techniques continuously works on facilitating emerging design approaches.

Optimization of management workflows must be reconsidered in order to facilitate new design approaches. As suggested by Open Systems Lab (2018), there is a high chance of failure in delivery of innovative design methods, such as described DfMA, if conventional procurement models are being used. Conventional procurements frequently create redundant cost based on the risk factor, particularly the risk of budget overruns. To illustrate the prevailing case of procurement where Design and Build contract is used, contingency budget adds between 10-20% to the cost of every building. Instead, the main principle of DfMA procurement is to place segments of traditional procurement in a different order (Figure 4).

Traditional procurement

DESIGN

ENGINEER

PRICE

COST

DESIGN

DfMA Procurement

ENGINEER

Figure 4 Procurement models (based on Open Systems Lab, 2018)

DfMA procurement works towards making a solution predictable, scalable and replicable in order to enhance the supply chain and facilitate innovative design methods (Open Systems Lab, 2018).

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Automated Construction 5.1 About Automated construction is one possible way to reduce the cost of the built environment. It has been characterized by constant inefficiencies despite its remarkable economic importance. It seems that the adoption of innovative automated systems within the construction industry has been very slow as to the alternative industries (e.g. automotive and consumer electronics sector) (Delgado, et al., 2019; Keating, et al., 2017). Automation of the construction process offers a wide range of possibilities. It simplifies logistics, reduces construction time and cost, decreases the complexity, affects labour cost and increases health and safety among the workforce (Keating, et al., 2017).

5.2 Challenges of Adoption of Automated Systems in Construction Despite the great potential, automated construction systems face numerous obstacles to wide-spread adoption within the industry. However, technological developments such as Building Information Modelling (BIM), sensing technologies and artificial intelligence foster the implementation of robotics in the construction industry (Delgado, et al., 2019). Given the above, Delgado, et al. (2019) have identified 4 categories of factors limiting the adoption of robotics in the construction industry: contractor-side economic factors, client-side economic factors, technical and work-culture factors and weak business case. In total, based on the literature review and qualitative survey, eleven factors that limit the adoption of robotics in the construction industry were determined. Figure 5 shows the correlation of listed factors in 4 different categories.

Factors limiting the adoption of robotics in the construction industry

CATEGORIES

FACTORS

Contractor-side economic factors

High initial capital investment Fragmented nature of the construction Low research and development (R&D) budgets in the construction industry

Easy access to labour No strong need to improve productivity Lack of government incentives Client-side economic factors

Decreasing public infrastructure budgets

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Technical and work-culture factors

Untrained workforce Unproved effectiveness/immature technology Current work culture/aversion to change

Weak business case

Low return on investment/insufficient demand

Figure 5 Factors limiting the adoption of robotics in the construction industry (based on Delgado, et al., 2019)

5.3 Contractor-side economic factors This category concerns all the costs of robotic implementation within the construction industry while excluding economic factors affecting the clients and infrastructure owners. First, “High initial capital investment” factor is ranked the highest within the category. Although the investment in automated systems undoubtedly becomes valid in the terms of increased productivity, it is not always feasible in practice. Since the sector is composed of many smaller-scale subcontracting companies, the financial resources needed for experimenting with innovative technologies are often accessible only to a few bigger-scale construction companies. Universal low profit margin in the construction industry creates high-risk ambience that might be a decisive factor in the survivability of the companies. Therefore, the central priority should be to lower the risk for construction companies which will result in justified initial high capital investment. Open Systems Lab (2018) argues that the reduction of risk can be resolved by using alternative procurement models such as DfMA procurement. Second, “No strong need to improve productivity” factor is tightly connected to the “Easy access to labour” factor. It has been determined that easy access to labour decelerates the adoption of automated systems. Even though the conventional construction faces enduring stagnation, this study shows that the motivation for improved productivity from the contractor side is low (Bock, 2015; Delgado, et al., 2019). However, this might change due to the reduction of human capital in developed nations caused by growing population ageing trends (Bock, 2015). Consequently, constrained accessibility to labour will eventually affect the “No strong need to improve productivity” factor. Third, “Lack of government incentives” also causes delay in the process of adaptation. This factor might gain more significance in the future taking into account the forecasts from Global Construction 2030 (2015). Essentially, infrastructure funding will have to find alternative sources other than public funding (Global Construction Perspectives and Oxford Economics, 2015). Fourth, “Fragmented nature of the construction” factor seems not to be vital for the adoption of robotics considering the low ranking. That comes as a surprise considering the complex supply-chain of construction projects delivery and the poor knowledge exchange among the stakeholders. Fifth, “Low R&D budgets in the construction industry” is also a lower ranked factor that characterizes the construction industry compared with other sectors such as manufacturing and automotive sectors (Delgado, et al., 2019).

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5.4 Client-side economic factors This category concerns all the costs of robotic implementation obtained by the client. “Decreasing public infrastructure budgets” factor suggests that limitation for adoption of automated systems in construction is characterized by the low infrastructure investments across the industrialised societies.

To illustrate the current trends, it has been observed that the infrastructure spending in 2017 was the lowest in the last 20 years. Governments, as the head clients of infrastructure spending, have direct influence on technological adoption within the construction industry. Limitation comes in the current tendering practice which usually awards “lowest price” as the primary factor in selection. In order to remain competitive and gain profit, construction companies restrict budgets for certain operational aspects (e.g. technological testing or ensuring material quality) (Delgado, et al., 2019; Open Systems Lab, 2018). In the terms of affordable housing provision, “lowest price” tendering criteria could be problematic as it can result in low-quality construction standards. Instead of reducing the construction performance quality, savings could be focused in other areas, such as construction time and waste. As presented, automated construction techniques have demonstrated significant improvements in addressing mentioned areas. Therefore, competitiveness and quality within the affordable housing market could be achieved by altering sources of profit for construction companies.

5.5 Technical and work-culture factors This category concerns the technical limitations of emerging technologies and work-culture related factors. First, “Unproved effectiveness/immature technology” factor indicates the doubts among stakeholders regarding the suitability of current technologies to be used in construction (Delgado, et al., 2019). Following the pace of technical development in the last 40 years, outcomes are evident in the form of increased activity within companies, research institutes, associations etc. Generally speaking, a new trend of adoption of innovative technologies is growing due to the acknowledgment and overall acceptance (Bock, 2015).

So far, the types of automation and robotic technologies for construction can be grouped in 4 major categories: Off-site prefabrication systems, On-site automated and robotic systems, Drones and autonomous vehicles, and Exoskeletons. Due to the early stages of development and experimentation with listed technologies, the main technical challenges were investigated for each category. The principal challenges of Off-site prefabrication systems “include the development of appropriate materials and the lack of understanding of the material mechanical performance” (Delgado, et al., 2019).

Furthermore, the technical challenges of On-site automated and robotic systems contain the lack of health and safety regulations and conflict with human workers activities in regard to unavailability to integrate. Next, the obstacles of Drones and autonomous vehicles preventing successful implementation among construction industry include high initial costs, low battery life, complex operability of hardware 23

and software, false perceived levels of accuracy and tolerances (which might result in fatal errors), stringent regulations that increase adoption cost and additional risks to health and safety. Lastly, the barriers of using Exoskeletons include the lack of health and safety regulations, usability concerns (e.g. durability, ruggedness, versatility etc.), the initial high cost and low acceptance rate by workers (ibid.).

Rapid technological development lacks the common set of metrics for intersystem comparison (Keating, et al., 2017). Hence, unproved effectiveness of automated systems is a justified technical factor that limits the adoption of robotics in the construction industry. For the purpose of measuring and comparing the effectiveness of different automated construction systems, Keating, et al. (2017) define two performance metrics (Figure 6). The first metric “Total work volume” (m3) estimates the scale of the structures that a given system can produce during a fabrication operation. The second metric “Typical volumetric fabrication rate” (m3/hour) measures how fast a given system can produce structures during a fabrication operation. The selected automated systems are in current development or have been developed since 2012 (ibid.). The variation of primary fabrication media was considered as the selection criteria.

Comparison of existing automated construction research Automated construction

DCP

Apis Cor

system name

Developer

Mediated

Apis Cor

Matter—

In-Situ

Guedel

BAAM

Fabricator

Gantry

Assembled

Robot

Architecture

ETH

ERNE

Cincinnati

Zurich

AG/ETH

Incorporated/Oak

Zurich

Ridge National

MIT

Flight

ETH Zurich

Laboratory Primary fabrication media

Spray-

Concrete

Brick

Timber

Thermoplastic

Foam block

foam

extrusion

assembly

assembly

565

396

9.75

52.2

4.42

57.7

2786

434

34.7

556

32.3

1000

1.728

0.375

0.176

0.718

0.033

0.375

Arm

Arm

Arm

Gantry

Gantry

Swarm

assembly

insulation formwork Largest fabricated structure

(m3)

Quantitative metrics

Total work volume

(m3)

Typical volumetric fabrication rate (m3/hour)

classification

metrics

Qualitative

System

Fabrication

(aerial) Extrusion

Extrusion

Assembly

modality

24

Assembly

Extrusion

Assembly

System

Mobile

Static

Mobile

Static

Static

Mobile

On-site

On-site

On-site

Off-site

Off-site

On-site

mobility Fabrication

Figure 6 Comparison of existing automated construction research (based on Keating, et al., 2017)

In summary, the initiatives to compare current automated systems exist and will continue to develop (ibid.). However, due to the recency of innovation, the overall construction industry still perceives it as uncertain.

Second, “Untrained workforce” factor characterizes the skill gap in construction workers as the industry is entering the Fourth Industrial Revolution. This factor is closely associated with “Current work culture/aversion to change” factor due to tremendous inertia and conservative implementation of new technologies in the construction industry (Delgado, et al., 2019; Keating, et al., 2017).

5.6 Weak business case This category concerns the lack of evidence that adopting robotics will lead to a cost reduction in the delivery of assets. Central “Low return on investment/insufficient demand” factor is justified since there are no detailed cost/benefit studies for adopting robotics reported in literature. In addition, the cost of investment does not involve only robotic mechanisms, but also the cost of software, skilled engineers and professional training. Therefore, the variable of time saving has also been underestimated.

Generally, studies are reporting time savings on individual automated tasks without taking into account the time needed for additional training and safety requirements. To summarize, the main issue is “whether the existing construction market structure and dynamics justify large capital investments in robotics” (Delgado, et al., 2019).

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Presentation of Research 6.1 About This section concerns the main research question and analysis of collected data. With the aim of answering “Can robotic automated technology contribute towards the cost reduction in the large-scale affordable housing construction?”, selected theoretical bases and the investigation on the practical case study were combined. The research is examining the case study of the construction technologies company ICON based on the previously analysed “Factors limiting the adoption of robotics in the construction industry” (Figure 5) (Delgado, et al., 2019).

ICON presents one example of a construction technologies company in the field of automated construction. ICON is an Austin-based construction technologies company that manufactures affordable housing by using additive manufacturing. ICON is currently targeting and operating within the economically developed market of the United States. The case study of ICON is selected based on the fact that their proof-of-concept house in Austin was the first permitted 3D-printed home to be built in the United States (Figure 7). Despite the recent establishment in 2018, ICON managed to attract numerous investors and manufacture units for the users in the urgent need for housing. Based on the short, yet rich experience, the example of ICON provides a resourceful insight for the emerging field of the automated construction industry (ICON, 2020).

Figure 7 Proof-of-concept 3D printed house in Austin (photo by R. Morton; source: ICON, 2020)

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Additive manufacturing (AM) is selected as an automated system in order to limit case study research.

AM â&#x20AC;&#x153;is defined as the process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologiesâ&#x20AC;? (Wohlers Associates, 2010). As the focused automated system in construction, AM has a potential to change the cost structure of the buildings to be based on the total raw material used instead of geometric complexity and design form.

Benefits of automated on-site manufacturing include reduction of the overall complexity between multiple trades,

increased worker safety, enhanced quality control and resilience to the local

environment (Keating, et al., 2017). In fact, extrusion construction methods dominated the general 3D printing construction market in 2018 in the United States. The size of the market was estimated to be USD 3 million in 2019 and reach USD 1.5 billion by 2024 (Markets Insider, 2019). Essentially, the growing figures justify the interest and examination of AM as the focused automated system.

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Case study through the factors 7.1 Contractor-side economic factors As stated, “High initial capital investment” is the biggest obstacle of implementing robotics in the construction industry. As a tech-startup, ICON was also exposed to the high-risk factor that many smallsize companies are facing. However, the entrepreneurial experience and educational background in conservation biology and mechanical engineering of ICON’s founders have allowed them to work around Vulcan 3D printer a few years before forming a company. After exhibiting the technology in 2018, ICON has established a further path through partnerships and attracting investments. Upon receiving a building permit for the first 3D printed house in the United States, the seed capital of USD 9 million fostered business growth and created additional opportunities (ICON, 2020). The seed funding was led by Oakhouse Partners, a venture capital investment firm focused on novel applications of emerging technologies with the potential to reinvent industries, such as blockchain, robotics, 3D printing etc. (Oakhouse Partners, 2020). Afterwards, ICON has allocated investments towards optimizing printers and creating a variety of home types and designs. Technological development has been proved by partnering with a non-profit organisation New Story on the 3D Printed Community project located in Tabasco, Mexico in 2019. Aligning the mission and core values of ICON and New Story have resulted in the successful partnership and delivery of 50 3D printed housing units (ICON, 2020). New Story aims to provide adequate housing for underprivileged populations affected by poverty and unsafe housing conditions through researching innovative housing construction solutions and expanding the knowledge within non-profit collective and governments (Grace, 2019; New Story, 2020). Shortly, internal organizational structure expansion and huge capital investments followed in August 2020. ICON secured USD 35 million in series A round of financing led by Moderne Ventures, an early stage venture fund aiming towards technology companies that are innovating within real estate and home services among other sectors (Moderne Ventures, 2020). Furthermore, ICON’s board of directors expands for two new members. Freedman of Moderne Ventures and Tasinga of Palantir, a software company that specializes in data analytics (Palantir, 2020; ICON, 2020).

Additive manufacturing, as a focused automated system, has demonstrated various benefits for increasing efficiency within the construction industry. However, the lacking need for improving productivity remains. In order to address the “No strong need to improve productivity” factor, the motivation of partners and investors has been researched. Freedman of Moderne Ventures (2020) agrees with ineffective construction methods and believes that “we will see an evolution of the entire homebuying value chain, especially when integrated with other technologies like digital transactions and augmented reality”. Architecture company Bjarke Ingels Group believes that investment and partnership with ICON can foster the transformation towards the future of construction by the aid of

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additive manufacturing. Ingels of Bjarke Ingels Group argues that there is a gap between digitally revolutionized design industry and conventional construction methods. Ingels states that “robotic manufacturing will enable us to eliminate the loss in translation from data to matter and allow us to fabricate homes at great speed, with less waste, and with higher accuracy” (cited in ICON, 2020). New Story, on the other hand, indicates the other factors for improving productivity. Due to the experience of affordable construction in the extreme environments and climates, their interests are allocated towards the technological capabilities of the printer in the challenging on-site conditions (New Story, 2020). Internally, ICON proves determined efforts to improve performance and productivity, particularly through technological development in robotics, software and advanced materials engineering (ICON, 2020). These activities are likely to influence further innovation within plumbing and electrical engineering in 3D printing which indicates that improved productivity will impact productivity within related sectors and collaborators (Peters, 2020).

“Easy access to labour” factor is often associated with the “No strong need to improve productivity” factor. Nevertheless, the framework of New Story (2020) emphasises the use of technology in crucial situations, such as natural disasters, when conventional construction labour cannot be performed. In that case, technology not only replaces inaccessible workers, but also increases health and safety conditions.

“Lack of government incentives” factor is particular in the case of ICON considering the secured building permit for the first 3D printed house in the United States in 2018 (ICON, 2020). The progress in implementation of robotics in the construction industry can be tracked in the areas where the government incentives were evident. For instance, the government of Dubai has set a goal of 3D printing 25% of every new building by 2030. So far, prototypes of the single-family dwellings have been 3D printed in China, Italy, Russia and Texas (Mims, 2018). At that time, Dr. Ben Carson, Housing and Urban Development Secretary, visits Austin prototype and notices the benefits of the innovative automated system for the areas affected by the natural disasters, such as reduced construction time (Claiborne, 2019). Also, Carson states that current housing policies must be revised and relocated to the local authorities who are familiar with the regional characteristics (Froehlich, 2019). For example, in Texas, there is no discretionary land use authority outside of municipal boundaries which means that social housing neighbourhoods can be located outside of city limits where there is no zoning (Mobile Loaves & Fishes, 2020). Ballard, Co-founder and CEO of ICON (cited in Claiborne, 2019), highlights that constraints in public housing policy can be solved if the technology and innovative approach is validated by the decision-makers in the world. Nevertheless, research shows that local governments are interested in adjusting and forming partnerships. In the case of 3D Printed Community project in Mexico, the partnership of New Story, ICON and ÉCHALE was supported by Mexican municipal and

29

state governments in purchasing the land, adding in necessary utilities to the community, and providing some financial assistance (New Story, 2020).

“Low research and development (R&D) budgets in the construction industry” factor is noteworthy in this case study since ICON and its partners are innovative emerging companies/organisations with the need to allocate funds towards research and development in order to foster productivity. ICON initiated R&D in 2018 immediately after completing prototype in Austin with the notable enhancement after securing USD 9 million in seed round of financing. Iterations and improvements on in-house technology resulted in delivering further projects and expanding the network of partners and investors (ICON, 2020). Following the recent expansion of ICON’s board of directors, it can be concluded that the serious measures towards R&D have been established. Palantir, a software company that specializes in data analytics, is particularly prominent when it comes to R&D in the field of their work. Their solutions address numerous sectors such as law enforcement, manufacturing, automotive, pharmaceutical, intelligence, etc. (Palantir, 2020). In addition, “Palantir is a substantial user of and contributor to opensource software (OSS)” (ibid.). Furthermore, boosted R&D can be followed based on the partnership with New Story on the 3D Printed Community project in Mexico. In June 2018, Goldman Sachs Gives awarded a grant to New Story for their R&D on the mentioned project. “The partnership with ICON and use of the 3D printing technology allows New Story to impact more families faster, while simultaneously improving quality and design flexibility. The hope is that this catalytic R&D project will influence the sector as a whole.” (New Story, 2020).

7.2 Client-side economic factors “Decreasing public infrastructure budgets” factor in the case study reflects on the forecast by Global Construction Perspectives and Oxford Economics (2015) that infrastructure funding will have to find alternative sources other than public funding. Despite determined efforts to deliver dignified housing to the vulnerable population and address the housing shortage issues, ICON was not initially sponsored by the governmental institutions. It seems that the mission values are not enough to obtain appropriate funds. Two main partners on the projects, New Story and Mobile Loaves & Fishes, are both non-profit organisations that secure funds through fundraising campaigns, charities and donations (Mobile Loaves & Fishes, 2020; New Story, 2020). ICON, on the other hand, possesses innovative technologies as the main asset which attracts tech investors. After delivering the first built prototype in 2018, ICON secured a seed round of USD 9 million that included investments from “Silicon Valley heavyweights”, international developers and big-scale homebuilders among others. Further technological advancements attracted more investors of the similar profile. Indeed, in series A round of financing, ICON secured USD 35 million. The recognition from governmental organisations, such as the U.S. Department of Housing and Urban Development (HUD), emerged after building a prototype and receiving building

30

permit for the first 3D printed house in the United States (ICON, 2020). In the meantime, the discussions on benefits of automated systems in construction were initiated with HUD Secretary Dr. Ben Carson (Claiborne, 2019). Due to the slow implementation of technological innovation and established housing regulations, the government cannot be an accountable client in equivalent cases as per Global Construction Perspectives and Oxford Economics (2015) predictions. It appears that decreasing public funds will be replaced by investments from technology-oriented stakeholders and eventually recognized and supported by local governments.

7.3 Technical and work-culture factors First, “Unproved effectiveness/immature technology” factor will be examined through technical analysis of ICON's technological platform and quantitative data comparison that indicates construction efficiency.

To start with, ICON's platform consists of a tablet-operated robotic printer, integrated material delivery system and cement-based material for building homes. The main asset, construction printer Vulcan, was developed by ICON in 2018. Substantively, it is a gantry system designed to precisely control the deposition of concrete over a large print area. The width of the printer can be adjusted in order to accommodate different slab sizes and it can be transported in ICON's custom trailer without assembly required. The complete Vulcan print system can be installed and operated with a workforce consisting of 3-4 people. On the technical specifications, Vulcan can print wall structures up to 2.5 m in height (Z) and foundations up to 8 m in width (X). Print length (Y) is infinite since Vulcan’s gantry system unlimitedly operates in Y direction. Correspondingly, described technical possibilities indicate that directions X and Z are limited. In addition, tested printing speed is up to 150 mm/s with a print bead size of 2.5 cm in height and 5 cm in width (ICON, 2020). In practice, prototype house in Austin with an area size of 33 m2 was printed in less than 48 hours by the first iteration of 3D printer Vulcan I. Second generation of Vulcan printer, Vulcan II, can print units up to 185 m2 in around 24 hours per unit (Powers, 2019).

Delgado, et al. (2019) advocate that one of the principle challenges of Off-site prefabrication systems “include the development of appropriate materials and the lack of understanding of the material mechanical performance” due to the immaturity of the overall field. ICON is responding to this challenge by developing Lavacrete, engineered Portland cement-based mix, which meets the International Building Code (IBC) structural code standards (ICON, 2020). It is extruded without bonding layers and the final aesthetic is described as a grey mass “with visible printing lines that determine the relief of a skin component” (Badalge, 2018). ICON (2020) states that each mix of Lavacrete can be programmed for specific attributes, meaning that it allows adoption to the local

31

building conditions and environment. Easy-to-source raw material with engineered additives provides widespread accessibility and workability.

Lavacrete comes with ICON's Magma system, an automated material delivery system used in 3D printed construction. Magma system mixes proprietary blend of Lavacrete, additives and water and supplies ready-to-print workable material to the Vulcan printer. Vulcan printer is, in that case, adjusted to the specific climate and situational conditions such as temperature, humidity, altitude and speed.

Manual work and measuring are reduced to minimum during on-site operations. However, printing is currently constrained by climate conditions since the insulation is not added by automated systems.

Therefore, it is must be conventionally placed into the cavity of the printed walls during construction process if the climate conditions require so (ICON, 2020).

Figure 8 Vulcan II 3D printer (source: ICON, 2020)

32

Figure 9 Vulcan 3D printer extruding Lavacrete (photo by R. Morton; source: ICON, 2020)

In the terms of housing construction, built projects in Austin and Mexico give an insight into the effectiveness of the production and fabrication. Overall, the automated system combines the installation of multiple components in the form of single-story structures. Foundation pouring and curing is followed by installation of two rails to the foundation edge. Afterwards, 3D printer Vulcan arrives onto the terrain slab and assures workability of the material via Magma delivery system. While Lavacrete is being extruded layer by layer on-site, Vulcan is controlled via tablet-based platform. Subsequently, finishing components such as roofs, windows, doors, plumbing installations and electrical wirings are completed using traditional construction methods (ICON, 2020; New Story, 2020).

On the 3D Printed Community project in Mexico, New Story and ICON have completed the first two simultaneously printed housing units after 18 months of planning. Each unit is about 46 m2 in size and takes around 24 hours to complete across several days. Provision of safely constructed units was crucial in order to ameliorate liveability. â&#x20AC;&#x153;The 3D printed homes feature two bedrooms, a living room, kitchen and bathâ&#x20AC;? and were designed with the feedback from the end users tailored to the community living needs (New Story, 2020).

33

Figure 10 Tabasco (Mexico) before the project development (photo by J. Gonzales; source: New Story, 2020)

Figure 11 Vulcan 3D printer (photo by J. Perez; source: New Story, 2020)

34

Figure 12 3D printed unit in Tabasco (Mexico) (photo by J. Perez; source: New Story, 2020)

Another exemplary project is Community First! Village in Austin developed by a non-profit organisation Mobile Loaves & Fishes. Community plot size of 206 390 m2 accommodates former members of Austin’s chronically homeless population. Phase I of the Village used to cover 109 265 m2 in plot size and accommodate around 200 formerly homeless citizens. Furthermore, “Phase I of the development includes 120 micro-homes (6 of them 3D printed), 100 recreational vans and 20 canvassided cottages” (Mobile Loaves & Fishes, 2020). Expansion towards Phase II in 2018 is directly adjacent to the Phase I development and expands in additional 97 124 m2 in plot size. Entire development has a capability to accommodate more than 500 formerly homeless citizens which represents about 40% of Austin’s chronically homeless population. In the partnership with Mobile Loaves & Fishes, ICON delivers 46 m2 central welcome unit and 6 3D printed units to the developing plot. Central welcome unit took a total of 27 hours to print over the several days (ibid.)

35

Figure 13 Creating six additional units for Community First! Village in Austin by using 3D printer Vulcan (photo by R. Morton; source: ICON, 2020)

Figure 14 Six 3D printed units upon completion as a part of Community First! Village in Austin (photo by R. Morton; source: ICON, 2020)

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ICON has addressed the second factor “Untrained workforce” by implementing a technology that can be used by the single trade of workers. In order to apply it to the case study, the competences and skills of ICON's team have been investigated. Also, research concerns on-site conditions for the workforce and the ease of using automated technology. Starting with the top management of ICON, all founders have entrepreneurial experience in common. Combination of interests and experience within sustainable building, automated construction and entrepreneurship reassured effective collaboration. ICON's team foresees expansion in order to integrate more professionals from the fields of robotics, off-planet construction, materials science, software engineering, architecture, building science and operations.

When it comes to the technological usability, ICON states that 3D printer Vulcan can be easily operated with a basic training thanks to the improvements in automation, mechatronics and a suite of specialized software. On-site conditions require a crew of 3-4 people to install and operate Vulcan and Magma print system (ICON, 2020). Clearly, the workforce will be reduced in amount and expected to demonstrate technological competences, such as programming, familiarity with fabrication techniques and material engineering.

Third factor, relating to the one above, “Current work culture/aversion to change” is addressed by ICON in the terms of creating a simplified and user-friendly attitude towards the innovative technological solutions. An example of streamlined technological solution is a control platform composed of an integrated tablet-based operating system which regulates print operations via an intuitive user interface.

Furthermore, research shows that ICON participated in South by Southwest conference where guided site visits to their proof-of concept house in Austin have been provided. Attendees had an opportunity to get familiar not only with the design and construction process, but also building challenges and future engineering initiatives (ICON, 2020; Powers, 2019). In addition, easily communicated projects and technical systems on ICON's website provide a rich database for fostering further knowledge exchange among interested individuals and stakeholders. Similarly, ICON's project partners are determined in their efforts to familiarize the public with the entrepreneurial background of their projects. Mobile Loaves & Fishes offers a quarterly symposium aiming for experience-based knowledge delivery through interactive talks, workshops, tours and other consultative guidance (Mobile Loaves & Fishes, 2020). Investigation of the background of ICON's projects indicates that appropriate partnership in combination with governmental support has an opportunity to reach wider public interest and attract attention of various groups, hence initiating the potential change.

7.4 Weak business case “Low return on investment/insufficient demand” factor is, as investigated, justified due to the lack of detailed cost/benefit studies for adopting robotics reported in literature. Comparison of construction

37

time, final cost figures and performance of ICON's proof-of-concept house in Austin with the USA national average will give an insight into the potentials and challenges within business.

Periodically conducted research by The National Association of Home Builders (NAHB) collects the information on the various components that go into the sales price of a typical single-family home in the United States. The median single-family home size in 2019 is around 240 m2 of the finished floor space which is the smallest value since 2011. The present decline from the peak in 2015 where the median size was around 254 m2 indicates that the contractors are leaning towards â&#x20AC;&#x153;the production of more entry-level homes to meet demand for more affordable homesâ&#x20AC;? (Ford, 2020). On the contrary, the average sales price of a newly built single-family home has been increasing steadily since the Great Recession. The striking increase from the average sales price of USD 267,900 in 2011 to the current national average of USD 485,128 in 2019 is 81%. The most recent survey on construction cost conducted by NAHB in 2019 demonstrates the total sales price of the single-family home is composed of 61% in construction costs, 18% in finished lot costs and 9% in builder profit (Figure 15) (ibid.).

Single-family homes sales price breakdown

construction cost

finished lot cost builder profit

Figure 15 Single-family homes sales price breakdown (based on Ford, 2020)

As a share of the median sales price, construction cost had greatly increased from 55% in 2017 to 61% in 2019. In parallel, the finished lot cost share fell from 21% to 18% and the average profit margin dropped from 10% to 9%. Figures indicate that developers spend increasingly on the housing construction itself, while the profit margin moderately decreases. The average construction cost of a typical single-family home in 2019 is around USD 296,652, or about USD 114 per square meter (ibid.).

The median construction cost of a single-family house in the United States is composed of various components (Figure 16) (ibid.).

38

Single Family House Price and Cost Breakdown 2019 National Results Average Lot Size: 2052 m2 Average Finished Area: 241 m2 1. Sale price breakdown

Average (USD)

Share of price

A) Finished lot cost (including financing cost)

89,540

18.5%

B) Total construction cost

296,652

61.1%

C) Financing cost

8,160

1.7%

D) Overhead and general expenses

23,683

4.9%

E) Marketing cost

4,895

1%

F) Sales Commission

18,105

3.7%

G) Profit

44,092

9.1%

Total sales price

485,128

100%

2. Construction cost breakdown

Average (USD)

I. Site work (sum of A to E)

18,323

Share of construction cost 6.2%

A) Building permit fees

5,086

1.7%

B) Impact fee

3,865

1.3%

C) Water & sewer fees inspection

4,319

1.5%

D) Architecture, engineering

4,335

1.5%

E) Other

719

0.2%

II. Foundations (sum of F to G)

34,850

11.8%

F) Excavation, foundation, concrete, retaining walls and backfill

33,511

11.3%

G) Other

1,338

0.5%

III. Framing (sum of H to L)

51,589

17.4%

H) Framing (including roof)

40,612

13.7%

I) Trusses (if not included above)

6,276

2.1%

J) Sheathing (if not included above)

3,216

1.1%

K) General metal, steel

954

0.3%

L) Other

530

0.2%

IV. Exterior Finishes (sum of M to P)

41,690

14.1%

M) Exterior Wall Finish

19,319

6.5%

N) Roofing

9,954

3.4%

O) Windows and doors (including garage door)

11,747

4%

P) Other

671

0.2%

V. Major System Rough-ins (sum of O to T)

43,668

14.7%

O) Plumbing (except fixtures)

14,745

5%

R) Electrical (except fixtures)

13,798

4.7%

S) HVAC

14,111

4.8%

T) Other

1,013

0.3%

VI. Interior Finishes (sum of U to AE)

75,259

25.4%

U) Insulation

5,184

1.7%

39

V) Drywall

10,634

3.6%

W) Interior trims, doors and mirrors

10,605

3.6%

X) Painting

8,254

2.8%

Y) Lightning

3,437

1.2%

Z) Cabinets, countertops

13,540

4.6%

AA) Appliances

4,710

1.6%

AB) Flooring

11,998

4%

AC) Plumbing fixtures

4,108

1.4%

AD) Fireplace

1,867

0.6%

AE) Other

923

0.3%

VII. Final Steps (sum of AF to AJ)

20,116

6.8%

AF) Landscaping

6,506

2.2%

AG) Outdoor structures (deck, patio, porches)

3,547

1.2%

AH) Driveway

6,674

2.2%

AI) Clean Up

2,988

1%

AJ) Other

402

0.1%

VIII. Other

11,156

3.8%

Total

296,652

100%

Figure 16 Single-family house price and cost breakdown (based on Ford, 2020)

Five components with the biggest share in the overall cost are: 1. Interior Finishes (25.4%) 2. Framing (17.4%) 3. Major Systems Rough-ins (Plumbing, Electrical, HVAC and other) (14.7%) 4. Exterior Finishes (14.1%) 5. Foundations (11.8%) ICON managed to significantly reduce the cost of some components within listed categories. Examples will be based on a proof-of-concept house in Austin (approximately 33 m2 in floor area) fabricated with Vulcan I and, if applicable, compared to the possible advancements of Vulcan II, second generation of Vulcan 3D printer. Vulcan II, developed in a single year from 2018 to 2019, has already delivered 3D printed housing in the past years with notable advancements (ICON, 2020; New Story, 2020). Hence it is relevant to track the technological progress for examining the business case.

First, overall “Interior Finishes” in the proof-of-concept house were done by using conventional construction techniques which suggests that the majority of the cost remains. However, “Drywall” can be excluded with a share of 3.6% since 3D printed walls do not need additional interior coverage (ICON, 2020; Powers, 2019).

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Second, “Framing” is the focused area since the cost of the printed portions are revealed. ICON's proofof-concept house in Austin, around 33 m2 in floor area, accounts the approximate cost of USD 10,000 for the printed portion of the overall construction excluding roof construction (Powers, 2019; Reggev, 2019). ICON claims that the same house with an improved version of Vulcan, Vulcan II, would be printed for the cost of USD 2,500 which is the 75% increase in technological efficiency in a single year (Powers, 2019). That means that the construction cost of framing excluding roof is USD 307 per m2 (Vulcan I) and USD 76 per m2 (Vulcan II). Meanwhile, conventional construction cost of framing for a single-family house is USD 51,589 including roof. If the median finished area amounts around 241 m2, the price of conventional framing including roof is USD 214 per m2. In comparison, performance of Vulcan I is more expensive than conventional method, while Vulcan II remains price competitive.

Considerable cost decrease by Vulcan II seems to be achieved by improving the time performance.

ICON's proof-of-concept house was printed by Vulcan I in 47 hours, while Vulcan II is claimed to complete the same job in a bit less than 24 hours (Powers, 2019; ICON, 2020). Assuming that the price of material remains the same within a year of Vulcan’s development, the savings can be assigned to the factors of time and speed.

Third, “Major Systems Rough-ins (Plumbing, Electrical, HVAC and other)” were done by using conventional construction techniques. ICON (2020) states that the team is looking into opportunities for engineering mentioned systems as integrated components. For now, this cost remains.

Fourth, “Exterior Finishes” cost is almost half reduced in a proof-of-concept house. Exposure of extruded Lavacrete eliminates the need for “Exterior Wall Finishes”. Waterproofing layer must be sprayed as a finish on top of extruded walls, but the need for additional materials is not required (ICON, 2020).

Fifth, “Foundations” mark the largest growth in cost, from 11% to 14% between 2017 and 2019 (Ford, 2020). Ford (2020) argues that this might be caused by the rising cost of ready-mix concrete seen during 2019. Vulcan I was not able to print foundations during the time of construction of proof-of-concept house in Austin. Due to the rapid technological development and upgrade to Vulcan II, traditionally performed foundation work is replaced with printed one. Not only foundations can be printed, but also footings. That process significantly reduces the overall time of construction (Ford, 2020; ICON, 2020).

Seemingly, the business case is not acknowledged yet due to the lack of built structures and comparable figures. The threat for the business case, noticed in “Framing” and “Foundations” section, is the use of concrete as a primary working material. Currently, it is the only structural material that can be 3D printed, yet it is unsustainable and expensive. As demonstrated, the principal benefits for the business case are time savings and construction speed increment.

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Conclusion To conclude, robotic automated technology provides a set of possibilities to contribute towards the cost reduction in the large-scale affordable housing construction. According to the presented research, significant construction time saving achieved by the automated technologies seem to be the key factor for lowering the cost of affordable housing construction. Notable reduction of time in the housing supply chain is not feasible by using conventional construction techniques. On the contrary, automated construction techniques provide prospective solutions regarding time saving in the housing supply chain.

In order for automated construction techniques to enter the AEC industry on the large scale, the business case must be reinforced. In other words, large capital investments in robotics must be justified. As seen, contractors are facing a fair amount of risk combined with the low profit margin. Instead of reducing the construction performance quality, savings could be focused in other areas, such as construction time and waste. As presented, automated construction techniques have demonstrated significant improvements in addressing mentioned areas. Therefore, competitiveness and quality within the affordable housing market could be achieved by altering sources of profit for construction companies.

Existing paradigm of housing supply is not adequate and must be altered towards emerging technological and digital development. It is important to mention the correlation of design and management factors in delivery of automated construction solutions. Design principles and project management execution will define the overall character of the construction. Likewise, design and management highly depend on each other. Optimization of management workflows must be reconsidered in order to facilitate new design approaches. Comparably, innovative design methods are prone to failure if conventional procurement models are being used.

Evidently, collaboration of stakeholders must adapt to different requirements. This could be achieved if all collaborating stakeholders are efficient users of automated solutions in practice and the legislative framework complies with the digital and technological needs of the projects.

The role of government is vital in the case of affordable housing. Decision-makers have to address the rising issues of expanding urban population, urban sprawl, housing shortage and unaffordability.

However, it appears that decreasing public funds will be replaced by investments from technologyoriented stakeholders and eventually recognized and supported by local governments. Investigation of the background of ICON's projects indicates that appropriate partnership in combination with governmental support has an opportunity to reach wider public interest and attract attention of various groups, hence initiating the potential change.

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It is important to bear in mind that these implications might vary based on the regional and regionaleconomic differences. The cost of variables that compose construction, such as labour, materials and framing, vary in different markets. Accordingly, automation within the construction industry offers different value in each market. Regardless, construction time saving universally remains the greatest contribution of automated systems to the cost reduction.

Finally, opportunities are various within automated construction due to the immaturity of the field and the lack of a common set of metrics for intersystem comparison. Efforts should be primarily directed towards material research and engineering since the current use of concrete proves to be unviable.

Apart from material development, other challenges of current automated systems could be addressed through research and development directives.

Acknowledgement and acceptance of innovative technologies keeps growing which results in faster adoption within the AEC industry (Bock, 2015). Combination of effective interdisciplinary collaboration, understanding of constraints and continuous technological advancements might offer prospective solutions in addressing urgent affordable housing issues.

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