Artificial intelligence technology applications in building construction productivity: A systematic literature review

Authors

DOI:

https://doi.org/10.38140/as.v30i2.7326

Keywords:

artificial intelligence, building construction, productivity, systematic literature review, technology

Abstract

Artificial Intelligence (AI) holds the potential to revolutionise the construction industry, by enhancing productivity and addressing the challenges posed by a skills shortage. Historically resistant to technological innovation, the construction sector lags behind other industries that have embraced innovative technologies to boost productivity. This study investigates AI technologies that can be used to improve construction productivity, as well as the barriers impeding the widespread adoption of AI in the construction sector. The research adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. A systematic review of scholarly journal articles and conference papers sourced from the Scopus database was conducted, employing relevant screening criteria to select the most pertinent sources aligned with the research objectives. Although AI applications in building construction are still emerging, AI technologies have been successfully deployed in various aspects of building construction. These include floor slab construction, steelwork, safety and risk management, materials management, and labour handling in multi-story buildings. The adoption of AI in the construction sector faces several challenges, including technical complexities, managerial and organisational barriers, economic justifications, and a shortage of AI-proficient talent. Drawing insights from this study, construction stakeholders can make informed decisions regarding AI investments and their specific areas of application within building construction.

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Published

2023-11-24

How to Cite

Adebowale , O. . and Agumba, J. . (2023) “Artificial intelligence technology applications in building construction productivity: A systematic literature review”, Acta Structilia, 30(2), pp. 161-195. doi: 10.38140/as.v30i2.7326.

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Section

Review articles