Artificial intelligence for occupational health and safety management in construction

Perera, Savindi; Paton-Cole, Vidal; Gao, Shang; Francis, Valerie; Urhal, Pinar; Manu, Patrick; Da Silva Bartolo, Paulo Jorge; Cheung, Clara; Yunusa-Kaltungo, Akilu; Babalola, Akinloluwa

Abstract

Reducing occupational safety and health (OSH) incidents has been an area of significant importance to the construction industry. The industry remains one of the most dangerous, with significant occupational fatalities and injuries. Artificial intelligence (AI), including deep learning and machine learning, shows promising potential to reduce injuries and avoid fatalities with the possibility for data acquisition of construction site activities and operations. To systematically assess the studies on AI aimed at improving construction safety, this research investigated 192 published journal articles (in English) within the Scopus database to determine the current research gaps and future work suggested by the publications. The analysis revealed a positive trend in publications in this area. Publications were also analysed based on the country of origin of the research and the host journal. The use of algorithms and the development of algorithms to address OSH issues were the most frequently used research methods, while the use of AI for visualisation and identification of hazards were the most frequent applications. Some research gaps and recommendations for future research are also discussed in the chapter.

• Publication Date: 2023
• Deposit Date: Jun 8, 2023
• Publisher: Taylor & Francis (Routledge)
• Pages: 154-168
• Book Title: Handbook of Construction Safety, Health and Well-being in the Industry 4.0 Era
• Chapter Number: 14
• ISBN: 9781003213796
• DOI: https://doi.org/10.1201/9781003213796-15
• Public URL: https://uwe-repository.worktribe.com/output/10848161
• Publisher URL: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003213796-15