Abstract
Machine learning (ML) has increasingly dominated discussions about the shape of mankind’s future, permeating almost all facets of our digital, and even physical, world. Yet, contrary to the relentless march of almost all other industries, the architecture, engineering and construction (AEC) industry have lagged behind in the uptake of ML for its own challenges. Through a systematic review of ML projects from a leading global engineering firm, this paper investigates social, political, economic, and cultural (SPEC) factors that have helped or hindered ML’s uptake. Further, the paper discusses how ML is perceived at various points in the economic hierarchy, how effective forms of communication is vital in a highly-specialized workforce, and how ML’s unexpected effectiveness have forced policy makers to reassess data governance and privacy; all the while considering what this means for the adoption of ML in the AEC industry. This investigation, its methodology, background research, systematic review, and its conclusion are presented.
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Acknowledgements
A huge thank you to the thirteen Arup interviewees who put aside time to share their knowledge and experience. Despite only a few being directly quoted, each conversation helped shaped the main ideas that drove this research. Thank you to Giulio Antonutto, Steven Downing, Veronika Heidegger, Jorke Odolphi, and Alvise Simondetti for sharing their experience with technological integration at Arup. Further, a big thank you to those in Arup University – Bree Trevena, Alex Sinickas, Esther Wheeler, and Kim Sherwin – without whom this research would not have been possible. Finally, thank you to both Arup Engineering and the University of New South Wales for their continual support.
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Khean, N., Fabbri, A., Gerber, D., Haeusler, M.H. (2019). Examining Potential Socio-economic Factors that Affect Machine Learning Research in the AEC Industry. In: Lee, JH. (eds) Computer-Aided Architectural Design. "Hello, Culture". CAAD Futures 2019. Communications in Computer and Information Science, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-13-8410-3_18
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DOI: https://doi.org/10.1007/978-981-13-8410-3_18
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