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Due to the frequent occurrence of subway construction safety accidents in recent years, construction workers face great safety threat, which produces serious social hazards. The causes of subway construction safety accidents are complex and require systematic causality analysis. Therefore, this study conducts a data-driven exploration, 209 subway construction safety accident investigation reports from 2005 to 2022 are collected as the original data, and the causal paths and intrinsic mechanisms of subway construction safety accidents are analyzed, aiming to improve the safety management ability and work safety in subway construction. Firstly, a total of 116 key features associated with subway accidents are extracted from the original data by text mining, which are utilized to develop an improved Human Factors Analysis and Classification System model (HFACS). Subsequently, a novel critical causal path screening model is proposed, by combining the Apriori algorithm in the association rules algorithm with gray relational analysis. Finally, the identified critical causal paths are verified using the Jaccard similarity calculation method, which demonstrates a high degree of similarity with the original accident data and provides empirical evidence of the effectiveness and rationality of the proposed model. This study provides a new perspective for exploring the causative factors of subway construction safety accidents and the complex interaction mechanisms. Cutting off the causal propagation in the accident causal paths can eliminate potential accidents and improve the safety of subway construction.
Huo et al. (Tue,) studied this question.