An integrated analysis method for critical human factors and paths in hazardous chemical storage accidents based on association rule mining and bayesian networks

Abstract

Hazardous chemicals possess significant inherent dangers, and accidents involving their storage can lead to severe consequences. Human factors are the primary contributors to such accidents; therefore, it is essential to conduct an in-depth study of the key human factors and critical pathways in hazardous chemical storage accidents to ensure the safe operation of hazardous chemical enterprises. This study proposes a combined research approach integrating an improved HFACS model, association rule mining, and Bayesian networks to perform a comprehensive analysis of accident case data, exploring causal relationships among human factors and identifying critical accident pathways. The results indicate that six highly sensitive human factors—resource management, organizational process, inadequate supervision, failure to correct problem, physical/mental limitations, and personal readiness—are the critical contributors to hazardous chemical storage accidents. Additionally, three critical paths leading to unsafe acts were identified: A1 → B1 → C3 → D1; C1 → D2; and A1 → B1 → C3 → D3. This study provides a novel approach for the quantitative analysis of human factors in hazardous chemical storage accidents and offers a new perspective for identifying key human factors and critical pathways through a data-driven methodology.