Risk analysis of bridge maintenance accidents:A two-stage LEC method and Bayesian network approach  

作　　者：Ting Fu Xinyi Li Wenxiang Xu Junhua Wang Lanfang Zhang Luochi Ye Rongjie Yu 

机构地区：[1]Key Laboratory of Road and Traffic Engineering,Ministry of Education,Tongji University,4800 Cao’an Highway,Shanghai,China [2]Hangzhou Innovation Institute,Beihang University,99 Juhang Lane,Changhe Street,Binjiang District,Hangzhou,Zhejiang,China [3]Hunan Communication Research Institute Co.,Ltd.,472 Mid Furong Road,Tianxin District,Changsha,Hunan,China

出　　处：《International Journal of Transportation Science and Technology》2024年第3期51-64,共14页交通科学与技术(英文)

基　　金：supported by the Shanghai Municipal Science and Technology Major Project of China(No.2021SHZDZX0100);the Shanghai Municipal Commission of Science and Technology Project of China(No.19511132101);the Fundamental Research Funds for the Central Universities of China.

摘　　要：Bridge maintenance is a long-term process that is prone to accidents.Identifying and reducing hidden dangers is crucial in decreasing the occurrence of such accidents.This study proposes a two-stage risk evaluation model based on the likelihood exposure conse-quence(LEC)method,which includes an occurrence stage and a development stage.The model utilizes hidden danger data accumulated over a long period to reflect the current maintenance stage’s risk level.Additionally,a risk prediction model based on the Bayesian network is established to better identify hidden dangers that have a significant impact on construction risk levels(CRLs).The models are validated using 50 weeks of hid-den danger data obtained from a real-world bridge maintenance project.The results show that certain hidden dangers have high risk levels when the CRL is high,and small changes in the risk level of certain hidden dangers can have a significant impact on the CRL.This study’s models can aid in the development of more targeted HD prevention measures.

关 键 词：Bridge maintenance Hidden danger Likelihood exposure consequence(LEC)method Bayesian network Risk evaluation and prediction 

分 类 号：R73[医药卫生—肿瘤]