Risk Assessment of Post-Earthquake Gas Explosion Disaster Chains in High-Rise Residential Buildings: A Fuzzy Bayesian and Complex Network Approach

To address the challenges in risk prevention and control of post-earthquake gas explosions in high-rise buildings and the deficiencies of traditional methods in handling uncertainty, this paper conducts a risk evolution analysis from the perspectives of fuzzy Bayesian networks (FBNs) and complex network (CN) theory. First, based on comprehensive risk factor identification, an earthquake-gas explosion disaster chain evolution model was constructed. Subsequently, the nodes and logical relationships of the disaster chain were mapped through Bayesian network (BN) topology, with fuzzy set theory employed to determine prior and conditional probability parameters for causal reasoning and risk diagnosis. Finally, complex network (CN) centrality metrics were introduced to quantify node topological importance, and chain-cutting disaster mitigation strategies were proposed accordingly. The research results indicate that gas overrun (M2) is the node with the highest comprehensive importance, while sensitivity analysis further confirms that it remains the most critical controllable node for interrupting the disaster chain. This method effectively reveals the disaster evolution mechanism and provides a scientific reference for disaster prevention and mitigation decision-making in high-rise buildings.