Risk assessment of goaf instability based on a variable weight model

Abstract

Goaf instability poses significant hazards, affecting mine safety and public welfare. This study aims to evaluate the risk of goaf instability to enhance safety measures in mining operations. Thirteen key indicators were identified to construct a comprehensive evaluation index system. By integrating game theory, we combined subjective and objective weights to develop a constant weight model, which was subsequently improved by considering data distribution characteristics to develop a local variable weight model. The variable weight intervals were determined through cumulative frequency analysis of normalized factor indices, and the Monte Carlo method was employed to define weight adjustment parameters. Using the cloud model, we assessed the instability risk of goafs. Our results indicate that the variable weight model provides higher evaluation accuracy compared to the constant weight model, offering clearer and more distinguishable membership degrees for the evaluation outcomes, suggesting its potential for more precise risk assessments in mining operations.