Unveiling Coupling Effects in Hazardous Liquid Pipeline Accidents: A Hybrid Data-Driven and Probabilistic Risk Assessment Framework

Abstract Understanding interactions among risk factors is essential for improving the safety of hazardous liquid pipelines. This study proposes a hybrid framework that integrates association rule mining with Bayesian network modeling to identify key risk factors and quantify their coupling effects. Historical hazardous liquid pipeline accident data are combined with meteorological and soil property data to support coupling-aware risk analysis. A fault tree is constructed and converted into a Bayesian network to evaluate the importance and sensitivity of accident factors across multiple failure scenarios. The results show that neglecting coupling effects leads to systematic bias in risk prioritization. Without coupling, pipeline monitoring system failure and shallow burial depth rank as the most influential factors, whereas incorporating coupling effects elevates internal microbial corrosion to the highest-ranked risk factor and introduces galvanic corrosion among the top contributors. Sensitivity analysis further indicates that corrosion-related and management-related factors exhibit markedly increased influence under coupled conditions, with distinct dominant drivers observed for leak, rupture, and mechanical puncture accidents. These findings demonstrate the critical role of factor coupling in accident propagation and show that explicitly modeling interaction effects improves causal transparency and supports targeted, proactive pipeline safety management.