Research on the Chain Evolution and Chain-Breaking Strategy of Expressway Damage Disasters Induced by Heavy Rainfall: Case Studies from Three Regions of China

The cascading damage of expressways induced by extreme heavy rainfall presents a persistent threat to transportation safety and regional sustainable development. To investigate the chain-like evolution characteristics of expressway damage caused by heavy rainfall and to identify precise strategies for mitigating disaster risks by breaking the chain. Firstly, directed causal event pairs were extracted, and clustering generalization was performed on disaster events.; the asymmetric Jaccard index was used to calculate edge weights, thereby establishing a directed causal knowledge graph of disaster chain evolution; Secondly, based on systematic risk assessment and chain-breaking priority indicators, we achieved the precise identification and quantification of critical vulnerable links; finally, we selected three typical damage cases—the ‘5·1’ case on the Meida Expressway in Guangdong, the ‘7·19’ case on the Danning Expressway in Shaanxi, and the ‘8·3’ case on the Yakang Expressway in Sichuan—for case validation, and proposed chain-breaking strategies. The research findings indicate that: (1) under specific hazard-forming environment, secondary disasters can supplant the primary causative factors to become the dominant driving nodes in chain evolution; (2) edge vulnerability and source-path diversity loss indicators respectively point to two distinct categories of high-risk edges; the comprehensive chain-breaking index compensates for the assessment blind spots of single indicators through two-dimensional weighting; (3) core vulnerabilities in disaster chains vary significantly across different regions: the Meida Expressway, the Danning Expressway, and the Yakang Expressway correspond to terminal response, pavement control node, and dual vulnerabilities at the source and structural levels, respectively, necessitating tailored chain-breaking strategies adapted to local conditions. These research findings offer a quantitative tool for infrastructure risk governance, contributing to the safety and sustainability of expressway transportation.