Stability risk early warning for mine goaf: Based on D-RES and asymmetric fuzzy connection cloud model

Abstract

To prevent and mitigate safety risks in the mining goaf and achieve risk prevention and shifting the control frontier forward, a novel goaf stability risk early warning model is developed based on D-RES and asymmetric fuzzy association cloud. Taking into consideration the intrinsic characteristics of the goaf, an indicator system covering 12 indicators from three aspects, namely goaf morphology, rock mass physical-mechanical parameters, and goaf development status, is constructed to assess goaf stability. To address the vagueness and randomness issues in the classification criteria for goaf stability early warning, a fuzzy interval-based model is proposed to quantify warning indicators and determine their fuzzy interval range and optimal boundary information. The RES theory is introduced to analyze the interaction relationships among warning indicators, and the D numbers theory is utilized to comprehensively synthesize opinions from multiple experts, establishing a D-RES coupled weight calculation model for collaborative decision-making. Additionally, the asymmetric fuzzy association cloud is introduced to represent the uncertainties and vagueness among different risk levels, considering the finite interval and asymmetric distribution features of warning indicator levels. By integrating the D-RES-derived coupled weights, early warning of goaf stability is achieved. The validation on mining goafs demonstrates that the warning results of the model are consistent with the actual safety conditions, indicating the high applicability and accuracy of the proposed early warning model. The developed model provides a theoretical foundation for formulating safety management policies relevant to mining goafs.