Coupling analysis of disaster-causing factors in coal mines and dual prevention mechanism based on the KeyBERT model and accident-causation theory model

The analysis of historical coal mine safety events and the accurate identification of disaster factors are essential for effective mine safety management. Based on the KeyBERT network model, conducts a coupling analysis of six typical coal mine disaster cases in China between 2013 and 2023: gas explosions, water disasters, fires, roof collapses, coal dust incidents, and rockbursts. It utilizes the 24Model (a theoretical model of accident causation) to systematically analyze the mechanisms of each causative factor. The research reveals that causative factors of coal mine accidents can be classified into three categories: geological factors representing hazardous conditions of materials, serving as prerequisites for disaster occurrences; behavioral and managerial factors reflecting unsafe human behaviors, crucial as trigger conditions for disasters. Moreover, deeply explored the disaster-causing characteristics and action mechanisms of key geological factors such as faults, folds, goafs and overburden structures, and divided behavioral factors into two levels: psychological and executive. It was found that psychological factors play a leading role in accident induction. When psychological factors are superimposed on problems at the executive level, major safety hazards will be formed, seriously threatening coal mine safety production. Based on these findings, we have developed a dual-prevention mechanism integrating hidden danger investigation with safety risk classification control, and proposed an innovative “3LA” coal mine disaster management system, revealing that the inevitability of mine disasters stems from simultaneous failures at three management levels.