This study aims to investigate and analyse the current knowledge and perspectives of AI applications within the construction industry in the UK, addressing a critical gap in the existing literature. The study aims to provide a deeper understanding of the industry’s need for digitalisation by understanding the views of construction industry professionals. The study uses a mixed-methods approach, utilising survey data from UK construction professionals analysed through descriptive, reliability, and analysis of variance tests. The results indicate a strong interest in artificial intelligence (AI) tools among construction industry professionals. Still, this interest is hindered by a lack of hands-on experience, leading to a lack of faith in AI’s capabilities. These findings provide a direction for integrating AI into the construction industry.
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Research Article|
August 26 2026
AI driven construction: unlocking efficiencies, improving safety and optimising outcomes
Nawaf Junaidi;
Nawaf Junaidi
School of Architecture, Building and Civil Engineering,
Loughborough University
, London, UK
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M. Sohail
;
M. Sohail
School of Architecture, Building and Civil Engineering,
Loughborough University
, London, UK
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Khaled Aljaber
School of Architecture, Building and Civil Engineering,
Loughborough University
, London, UK
Corresponding author Khaled Aljaber (khaled.aljaber@outlook.com)
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Corresponding author Khaled Aljaber (khaled.aljaber@outlook.com)
Publisher: Emerald Publishing
Received:
December 24 2024
Accepted:
July 16 2026
Online ISSN: 2397-8759
© 2026 Emerald Publishing Limited
2026
Emerald Publishing Limited
Licensed re-use rights only
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction 1–16.
Article history
Received:
December 24 2024
Accepted:
July 16 2026
Citation
Junaidi N, Sohail M, Aljaber K (2026;), "AI driven construction: unlocking efficiencies, improving safety and optimising outcomes". Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1680/jsmic.24.00041
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