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

Bridge maintenance is a long-term process that is prone to accidents. Identifying and reducing hidden dangers (HDs) is crucial in decreasing the occurrence of such accidents. This study proposes a two-stage risk evaluation model based on the LEC method, which includes an occurrence stage and a development stage. The model utilizes HD 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 HDs that have a significant impact on construction risk levels. The models are validated using 50 weeks of HD data obtained from a real-world bridge maintenance project. The results show that certain HDs have high risk levels when the construction risk level (CRL) is high, and small changes in the risk level of certain HDs can have a significant impact on CRL. This study's models can aid in the development of more targeted HD prevention measures.