Accurately predicting the carbonation depth of recycled aggregate (RA) concrete is essential for durability assessment. Based on a dataset of 682 experimental samples, this study employed seven machine learning algorithms to develop prediction models for the carbonation depth of RA concrete. The Optuna framework was utilized to conduct 500 trials of hyperparameter optimization for these models, with the objective of minimizing the 5-fold cross-validated mean squared error. Results indicate that model performance improved significantly after optimization. Among them, the XGBoost model achieved the best performance, with a coefficient of determination (R²) of 0.9789, root mean squared error (RMSE) of 1.0811, mean absolute error (MAE) of 0.6972, mean absolute percentage error (MAPE) of 8.7932%, variance accounted for (VAF) of 97.8966%, and mean bias error (MBE) of 0.0641 on the test set. Explainability analysis using SHapley Additive exPlanations (SHAP) further revealed that exposure time is the most significant factor influencing the carbonation depth prediction. Additionally, considering that the database incorporates both natural and accelerated carbonation conditions, the samples were partitioned based on CO₂ concentration and conducts a stratified performance evaluation. The results demonstrate that the model maintains high predictive accuracy under natural carbonation as well as across different accelerated carbonation intervals, indicating that, within the scope covered by the current dataset, the proposed approach provides a highly accurate and interpretable tool for predicting the carbonation depth of recycled aggregate concrete.

With the advancement of mining technologies, the evaluation of rock diggability has become a critical research topic for ensuring both safety and efficiency in mining operations. This study establishes a comprehensive evaluation system for mine rock diggability and proposes corresponding grading criteria. For the determination of indicator weights, a combination of subjective and objective methods is employed, integrating expert knowledge and data characteristics to identify optimal weights, thereby providing a reliable basis for comprehensive evaluation. The single-indicator cloud model effectively mitigates the difficulties associated with defining transitional values between adjacent intervals. The multidimensional cloud model, by considering the interactions among indicators, enables the optimization of indicator interactions and enhances the interpretability of diggability grades. Comparison with the Diggability Index (DI) method shows a high consistency between the two approaches (R² = 0.991). The absolute accuracy of diggability levels reaches 74%, while the accuracy based on cloud model fuzzy evaluation reaches 100%, demonstrating the effectiveness of the cloud model in handling transitional intervals and capturing uncertainty. This study provides a novel methodology and theoretical foundation for the scientific evaluation of mine rock diggability, offering practical guidance for reasonable grading, optimization of mining parameters, and interpretation of diggability levels in engineering practice.

Cylindrical charges are widely used in engineering blasting, yet the three-dimensional propagation mechanism of the associated stress waves remains inadequately understood. This study aims to investigate the effects of key wave source parameters on stress wave propagation and rock damage in cylindrical charge blasting. A semi-analytical solution for spherical stress wave propagation in a full elastic space is developed to theoretically describe the stress field, and a computational model for cylindrical charges is established based on the superposition principle of equivalent spherical charges. Numerical simulations using the RHT constitutive model are then performed to verify the theoretical predictions and further investigate stress wave propagation and rock damage. The results show that the attenuation index of radial stress decreases from 1.5 to 1 as the loading rate increases. Higher loading rates produce more but shorter cracks, whereas lower rates result in fewer but longer cracks. The blast-induced damage region shifts from the detonation direction toward the horizontal plane with increasing detonation velocity, and the resulting rock damage exhibits a conical distribution controlled by the initiation point. These findings provide practical guidance for optimizing cylindrical charge blasting and controlling crack patterns in engineering applications.

The frequent blasting in underground mines results in stress waves of different intensities, which is one of the main factors leading to backfill collapse. Improving the strength of backfill is an effective way to reduce the backfill damage. In this study, rice straw fiber and graded tailings were used as raw materials to prepare rice straw fiber-reinforced cemented tailings backfill (RSCTB). An orthogonal experimental design was employed to perform unconfined compressive strength (UCS) tests, diffusivity measurements, and Split Hopkinson Pressure Bar (SHPB) tests. The results showed that straw fibers slightly reduce slurry fluidity. The UCS of RSCTB at a specific mix ratio was more than 50% higher than that of cemented tailings backfill (CTB) without rice straw. The dynamic unconfined compressive strength (DUCS) of RSCTB increased linearly at different strain rates. The effect of rice straw fibers on the UCS and DUCS was much smaller than that of cement content and solid mass concentration. Excessively long and abundant straw fibers are not conducive to improving the long-term impact resistance of RSCTB.