A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Causal Analysis of Subway Construction Safety Accidents Based on Random Forest
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.
Causal Analysis of Subway Construction Safety Accidents Based on Random Forest
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.
Causal Analysis of Subway Construction Safety Accidents Based on Random Forest
Lecture Notes in Operations res.
Li, Dezhi (editor) / Zou, Patrick X. W. (editor) / Yuan, Jingfeng (editor) / Wang, Qian (editor) / Peng, Yi (editor) / Du, Shuang (author) / Huo, Xiaosen (author) / Jiao, Liudan (author) / Hao, Tong (author)
International Symposium on Advancement of Construction Management and Real Estate ; 2023 ; Jiulonghu Hotel, Nanjing, China
2024-11-28
16 pages
Article/Chapter (Book)
Electronic Resource
English
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