Zhang Guowei, Ren Ting, Han Jun
College of Mining, Liaoning Technical University, Fuxin, 123000, China.
University of Wollongong, Wollongong, NSW, 2522, Australia.
Sci Rep. 2025 Feb 10;15(1):4884. doi: 10.1038/s41598-025-85988-y.
The prevention and management of coal mine roof accidents remain challenging issues because it is difficult to evaluate and quantify the interaction effects of the disaster hazard factors objectively. This paper proposes a novel approach: combining information entropy and the surrogate model-and applies Sobol's method, aiming to solve it and to obtain the hazard factors' 1th and the global sensitivity value without human intervention. The results show that: (1) The complex logical relationships and interactions of roof hazard factors can be transformed into quantifiable numerical values by building a co-occurrence matrix of disaster factors and calculating its information entropy. (2) The sensitivity levels of roof hazard factors can be successfully distinguished and categorized into priority management and prevention or general management and prevention using the surrogate model and Sobol's sensitivity method. The novel sensitivity analysis approach suggested in this study considers both the individual impacts of hazard factors and their interactions, offering a more thorough framework for risk assessment as well as a fresh perspective and tool for coal mine safety research.
煤矿顶板事故的预防与管理仍然是具有挑战性的问题,因为难以客观地评估和量化灾害危险因素的相互作用影响。本文提出了一种新方法:将信息熵与代理模型相结合,并应用索博尔方法,旨在解决该问题并在无需人工干预的情况下获得危险因素的一阶和全局敏感性值。结果表明:(1) 通过构建灾害因素共现矩阵并计算其信息熵,可将顶板危险因素的复杂逻辑关系和相互作用转化为可量化的数值。(2) 使用代理模型和索博尔敏感性方法,能够成功区分顶板危险因素的敏感性水平,并将其分为优先管理预防或一般管理预防类别。本研究提出的新型敏感性分析方法既考虑了危险因素的个体影响,也考虑了它们之间的相互作用,为风险评估提供了更全面的框架,也为煤矿安全研究提供了新的视角和工具。