Rock mass quality classification (RMQC) plays a crucial role in rock mass stability analysis and in the design and construction planning of rock engineering projects. However, current RMQC methods rely on expert experience, which makes it difficult for RMQC to be intelligent, scientific, and interpretable, and is not conducive to understanding rock mass characteristics in engineering applications. Therefore, this study proposes an interpretable rock mass quality intelligent classification model (IRICM) by coupling random forest (RF) and genetic algorithm (GA) to refine decision rules, aiming to enhance the intelligence, scientificity, and interpretability of RMQC. Based on 318 tunnel section data, the RMQC dataset was constructed using rock mass rating (RMR) parameters obtained from field investigations and laboratory experiments. By coupling RF and GA, the rules from all decision trees were selected, combined, and optimized to refine decision rules, achieving a classification accuracy of 87.50 % with only five rules per class. Interpretability analysis of the refined decision rules revealed that rock quality designation (RQD), intact rock strength (IRS), joint spacing (JS), and groundwater (GW) were the most frequently used features, confirming their importance in RMQC. Further analysis using post-hoc interpretability techniques also indicated that RQD, IRS, JS, and GW contributed most significantly to RMQC, especially in distinguishing poor rock mass quality (classes IV and V). The model was applied to the RMQC of tunnels and rock slopes, and the results demonstrated consistency with classification outcomes from the Q, RMR, and geological strength index (GSI) systems, validating its reliability and stability.
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