Safety Risk Assessment of Jacking Renovation Construction for Aging Bridges Based on DBN and Fuzzy Set Theory

Abstract

The jacking renovation construction of aging bridges faces significant safety risks due to the complexity and uncertainty of their structures. Addressing the limitations of traditional risk assessment methods in handling dynamic changes and data scarcity, this study proposes a safety risk assessment approach based on dynamic Bayesian networks (DBN) and fuzzy set theory (FST). By using DBN to model the temporal evolution of risks, combined with the Leaky Noisy-OR Gate extension model and FST to quantify expert knowledge, this method overcomes the constraints of insufficient data. Taking an elevated bridge jacking renovation project in Qingdao, China, as a case study, a risk indicator system was established, incorporating factors such as personnel, equipment, and the environment. The results show that risks are higher in the early stages of construction and stabilize later on, with poor foundation conditions, instability of the substructure, and improper operations identified as key risk sources requiring focused control. Through forward reasoning, the study predicts risk trends, while backward reasoning identifies sensitive factors, providing a scientific basis for construction safety management.