Causal Analysis of Subway Construction Safety Accidents Based on Random Forest

Shuang Du (), Xiaosen Huo, Liudan Jiao and Tong Hao
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Shuang Du: Chongqing Jiaotong University
Xiaosen Huo: Chongqing Jiaotong University
Liudan Jiao: Chongqing Jiaotong University
Tong Hao: Chongqing Jiaotong University

Chapter Chapter 68 in Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, 2024, pp 1005-1020 from Springer

Abstract: Abstract In recent years, safety accidents during subway construction have been occurred frequently in China, resulting in significant casualties and property losses. To minimize the losses caused by such accidents effectively and identify key factors influencing subway construction safety, this study employs the Human Factors Analysis and Classification model (HFACS). Using 107 typical subway construction safety accident investigation reports as data samples, the study constructs an HFACS model that is applicable to subway construction safety accidents, and screens out 21 accident factors. Additionally, a prediction model for subway construction safety accident levels is developed using the Random Forest method to rank the accident factors in terms of importance. The results show that the accuracy of the prediction model using Random Forest reaches 0.88, and five key factors that have a greater impact on accident severity are identified. Based on these findings, the study puts forward targeted pre-control measures and recommendations to reduce the probability of subway construction accidents effectively.

Keywords: Subway construction; Human factors analysis and classification model (HFACS); Random forest; Accident severity (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-1949-5_69

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