RSRI-Based Modeling of Coal Mine Gas Explosion Accident Causation Networks

Coal mine gas explosions remain a major occupational hazard, driven by the interaction of multiple risk factors. In this study, a systematic framework was developed for accident causation analysis and prevention by integrating root–state risk identification (RSRI) theory with complex network modeling. An analysis of 102 accident reports identified 112 primary risk factors, which were incorporated into a causation network. Nodes were prioritized through entropy-weighted TOPSIS, and edge vulnerability analysis was applied to reveal dominant evolutionary pathways. The results indicate that gas accumulation in the heading face constitutes the most critical direct cause, while insufficient safety supervision is the principal indirect driver. The most hazardous pathway involves inadequate ventilation inspection, reduced air supply, gas accumulation, weak supervision, limited safety training, and unsafe blasting practices. These findings underscore the pivotal role of organizational and behavioral deficiencies in risk propagation. The proposed framework advances current approaches to risk assessment by systematically identifying key factors and critical paths, thereby providing actionable insights for enhancing supervision, strengthening preventive strategies, and reducing catastrophic accidents in coal mines.