西安科技大学学报2025，Vol.45Issue(2)：286-295,10.DOI:10.13800/j.cnki.xakjdxxb.2025.0207
基于组态分析与机器学习的煤矿运输事故致因研究
Research on the causes of coal mine transportation accidents based on configurational analysis and machine learning
摘要
Abstract
To address the frequent occurrence of coal mine transportation accidents and prevent further deterioration,this paper studied 68 coal mine transportation accidents.It constructed a causation model and attribute table for these accidents based on an improved HFACS theory.Using crisp-set qualitative comparative analysis and random forest methods,the paper performed a comprehensive analysis of the causes of the accidents,establishing an integrated analysis framework combining configurational analy-sis and machine learning.Finally,the key factors preventing accident occurrence were revealed using SHAP values,and the robustness of the configurational analysis was validated.The results indicate that:The configurational analysis identifies 12 configurations of coal mine transportation accidents,clus-tering into such three high-level configurations as technical defects,inadequate supervision,and lack of safety education.The random forest model achieves an overall accuracy of 92.9%,with particularly high precision and recall rate in predicting accident occurrence(category 1).The SHAP value scatter plot of the model shows that technical environment,inadequate supervision,and organizational atmos-phere are core conditions leading to accidents,further validating the robustness of the configurational a-nalysis.Based on the study results,preventive and responsive measures for the high-level configuration paths inducing coal mine transportation accidents are proposed.关键词
清晰集定性比较分析/组态致因模型/机器学习/煤矿运输事故Key words
crisp-set qualitative comparative analysis/configurational causality model/machine learn-ing/coal mine transportation accidents分类
矿山工程引用本文复制引用
钱敏,王哲,成连华..基于组态分析与机器学习的煤矿运输事故致因研究[J].西安科技大学学报,2025,45(2):286-295,10.基金项目
国家自然科学基金项目(51974238) （51974238）
陕西省自然科学基础研究计划项目(2021JM-397) （2021JM-397）
