Rockburst risk assessment based on multi-parameter critical indicators: CW-TOPSIS approach

Rockburst occurrence is governed by multiple interacting factors. To improve rockburst risk assessment in coal mines, five key parameters—critical softening radius, critical load, critical convergence displacement, critical energy, and critical mining depth—were selected as critical indicators, and a CW-TOPSIS-based method for rockburst risk assessment using multi-parameter critical indicators was proposed. A three-tier evaluation framework comprising the target, primary-indicator, and secondary-indicator layers was established through theoretical analysis. The analytic hierarchy process (AHP) and entropy weight method (EWM) were used to determine the subjective and objective weights of the indicators, respectively. These weights were integrated using a Euclidean distance optimization method to obtain the combined weights, which were subsequently used to construct a weighted decision matrix. The combined-weight Technique for Order Preference by Similarity to Ideal Solution (CW-TOPSIS) model was then employed to calculate the relative closeness coefficients and construct a rockburst risk assessment space for risk-level classification. The proposed method was applied to the 4207 working face of a coal mine. The results classified the working face as having a low rockburst risk, consistent with the result obtained using the comprehensive index method. Compared with the comprehensive index method, the proposed method provides a more quantitative representation of rockburst risk in terms of both numerical evaluation and risk-level classification. This study provides a quantitative approach for rockburst risk assessment in coal mines.