Machine learning algorithms for safer construction sites: Critical review

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Keywords: machine learning (ML); construction; risk management; critical review; Litmaps®; Open knowledge maps®


Machine learning, a key thruster of Construction 4.0, has seen exponential publication growth in the last ten years. Many studies have identified ML as the future, but few have critically examined the applications and limitations of various algorithms in construction management. Therefore, this article comprehensively reviewed the top 100 articles from 2018 to 2023 about ML algorithms applied in construction risk management, provided their strengths and limitations, and identified areas for improvement. The study found that integrating various data sources, including historical project data, environmental factors, and stakeholder information, has become a common trend in construction risk. However, the challenges associated with the need for extensive and high-quality datasets, models’ interpretability, and construction projects’ dynamic nature pose significant barriers. The recommendations presented in this paper can facilitate interdisciplinary collaboration between traditional construction and machine learning, thereby enhancing the development of specialized algorithms for real-world projects.


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How to Cite
Junjia, Y., Alias, A. H., Haron, N. A., & Abu Bakar, N. (2024). Machine learning algorithms for safer construction sites: Critical review. Building Engineering, 2(1), 544.