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

Abstract

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 consequence (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 hidden 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.