Blasting is an essential operation in surface mining, but it can generate high particulate matter concentrations within a short period. Accurate prediction of blast-induced dust concentration is useful for air quality management, worker protection, and dust control planning. In this study, an interpretable machine learning framework was developed to predict particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) and total suspended particulate matter (TSP) concentrations induced by blasting in a large surface coal mine. The dataset was derived from published field monitoring records and included blasting design parameters, monitoring distance, material-related variables, and measured dust concentrations. A total of 148 valid samples were used for model development and evaluation. Six tree-based ensemble models, including Extra Trees Regression, Random Forest, Gradient Boosting Regression Trees, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were established and compared. Bayesian optimization was used for hyperparameter tuning, and model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Under the adopted 80:20 hold-out validation scheme, CatBoost achieved the best test performance for PM₁₀ prediction, with a test R² of 0.9555, RMSE of 444.87 μg/m³, and MAE of 343.48 μg/m³. Extra Trees Regression performed best for TSP prediction, with a test R² of 0.8803, RMSE of 3899.04 μg/m³, and MAE of 3036.27 μg/m³. Residual analysis further indicated that the optimal models had no obvious systematic bias. Shapley additive explanations (SHAP) analysis, supported by within-model feature importance rankings, showed that explosive quantity, number of blastholes, and monitoring distance were the dominant variables affecting PM₁₀ and TSP predictions. The explosive quantity and number of blastholes mainly increased the predicted dust concentration, whereas the monitoring distance generally reduced it. The proposed framework may provide useful support for blast parameter optimization, monitoring point arrangement, and dust control decision-making in surface mines, but further validation using larger multi-site datasets is still needed.

Rock drillability classification provides an important basis for drilling-parameter optimization, equipment selection, and improved mining efficiency. Existing drillability evaluation methods often rely on fixed empirical weights and rigid grade boundaries, making it difficult to capture fuzzy transitions between adjacent grades under multi-indicator geological conditions or to explain classification deviations for boundary samples. This study proposes a quantitative rock drillability classification method that integrates FDAHP-based subjective weighting, objective weighting, and a multidimensional cloud model. A 12-indicator evaluation system is first established by considering rock physicomechanical properties, rock-mass structural conditions, and drilling-response characteristics. FDAHP is then used to derive subjective weights from judgment matrices provided by five experts, while the entropy weight method, CRITIC method, and coefficient of variation method are used to obtain objective weights. These weights are combined into a subjective–objective weighting scheme and incorporated into a multidimensional cloud model to represent the fuzziness and randomness of drillability grade boundaries. For incomplete-indicator samples, the comprehensive weights are projected onto the available indicator subset and renormalized, avoiding forced imputation of missing indicators. Validation using 15 complete samples and seven incomplete-indicator samples from the Sungun copper mine shows that the proposed combined weighting method achieves an accuracy of 93.33% for complete samples and correctly classifies six of seven incomplete-indicator samples, with an accuracy of 85.71%. The cloud-model analysis of the misclassified sample indicates that it lies near an adjacent-grade boundary, providing an interpretable explanation for its classification uncertainty. These results suggest that the proposed method can provide interpretable quantitative drillability classification for the Sungun case study and may support preliminary field drillability assessment under incomplete-information conditions.