Improving Safety Performance of Construction Workers through Learning from Incidents

Albert P C Chan; Junfeng Guan; Tracy N Y Choi; Yang Yang; Guangdong Wu; Edmond Lam

doi:10.3390/ijerph20054570

Improving Safety Performance of Construction Workers through Learning from Incidents

Int J Environ Res Public Health. 2023 Mar 4;20(5):4570. doi: 10.3390/ijerph20054570.

Authors

Albert P C Chan^{1

2}, Junfeng Guan¹, Tracy N Y Choi², Yang Yang^{1

2}, Guangdong Wu³, Edmond Lam⁴

Affiliations

¹ Shenzhen Research Institute of the Hong Kong Polytechnic University, Shenzhen 518057, China.
² Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China.
³ School of Public Policy and Administration, Chongqing University, Chongqing 400044, China.
⁴ College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China.

Abstract

Learning from incidents (LFI) is a process to seek, analyse, and disseminate the severity and causes of incidents, and take corrective measures to prevent the recurrence of similar events. However, the effects of LFI on the learner's safety performance remain unexplored. This study aimed to identify the effects of the major LFI factors on the safety performance of workers. A questionnaire survey was administered among 210 construction workers in China. A factor analysis was conducted to reveal the underlying LFI factors. A stepwise multiple linear regression was performed to analyse the relationship between the underlying LFI factors and safety performance. A Bayesian Network (BN) was further modelled to identify the probabilistic relational network between the underlying LFI factors and safety performance. The results of BN modelling showed that all the underlying factors were important to improve the safety performance of construction workers. Additionally, sensitivity analysis revealed that the two underlying factors-information sharing and utilization and management commitment-had the largest effects on improving workers' safety performance. The proposed BN also helped find out the most efficient strategy to improve workers' safety performance. This research may serve as a useful guide for better implementation of LFI practices in the construction sector.

Keywords: Bayesian network; construction industry; learning from incidents; safety learning; safety performance.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Causality
China
Construction Industry*
Humans
Occupational Health*
Safety Management
Surveys and Questionnaires

Grants and funding

This paper forms part of a research project funded by the National Natural Science Foundation of China (No. 71971186), from which other deliverables will be produced with different objectives/scopes but sharing common background and methodology.