A coal mine gas explosion is a systematic failure caused by the interaction of multiple factors. In previous studies, most research determined the key causes based on practical experience or a single static indicator. This study puts forward a comprehensive method that integrates complex network theory and a genetic algorithm. By analyzing the explosion mechanism, a network model with 43 causal factors as nodes and their relationships as edges was established, thus capturing the overall structure of the accident system. Subsequently, the genetic algorithm was employed to optimize the identification of key nodes in the network. At present, most of the research on accident risk assessment relies on static topological analysis, failing to take into account the synergistic effects resulting from the simultaneous removal of multiple nodes, and is prone to getting stuck in local optimal solutions. The purpose of this study is to be able to search for the most influential node set and reduce the reliance on static indicators. The results show that both random attacks and deliberate attacks can reduce network efficiency. Meanwhile, when attacking the key cause combinations identified through searching, the network efficiency drops most rapidly. This indicates that the network is more vulnerable in more targeted attacks. This method encourages us to transition from a single-dimensional risk assessment to a comprehensive and multi-dimensional analysis framework.
Miao et al. (Thu,) studied this question.