146 publications from this institution
Abstract Based on three‐dimensional digital image correlation (3D‐DIC) technique, the deformation behavior, damage and fracture characteristics of the granite specimens have been tested and observed by the short core in compression (SCC) method. The test results show that (1) the damage degree of rock in the region of interest (ROI) increases with axial stress. Before peak stress, damage factor based on apparent principal strain increases slowly, then rises rapidly; (2) relative displacements in both horizontal and vertical directions are observed, indicating that the fracture mode for SCC specimens subjected to uniaxial compression is the tensile‐shear mixed mode fracture; (3) new fracture initiates from the middle part of the expected shear fracture band (ESFB) then propagates along the loading direction. A dominant macro‐fracture, linking up the tips of two notches, eventually forms. Meanwhile, a calculation method based on the energy conservation law for the fracture energy of SCC specimens is proposed. The fracture energy of granite is estimated as 1760.4 J/m 2 .
Accurate and efficient rock mass quality classification is a prerequisite for assessing slope stability, designing support schemes, and ensuring mining safety in open-pit mines. However, traditional empirical classification methods rely heavily on expert judgment and often struggle to capture the complex, nonlinear relationships among factors influencing slope stability. Existing intelligent classification models also suffer from limitations, including sensitivity to incomplete data, insufficient feature interaction learning, and unstable performance on small-scale datasets. To address these issues, this study develops a deep forest (DeepForest) model optimized by three metaheuristic algorithms—brown bear optimizer (BBO), tuna swarm optimizer (TSO), and sparrow search algorithm (SSA)—to intelligently classify slope rock mass quality. A rock mass quality dataset containing 204 groups of slope and non-slope cases was established to train and evaluate the classification performance of the DeepForest models. Six influencing factors were set as input parameters: uniaxial compressive strength (UCS) of rock, rock quality designation (RQD), spacing of discontinuities (Sd), rock mass integrity coefficient (Kv), groundwater conditions (W), and site type (St). Multivariate imputation by chained equations (MICE), isolation forest (IsoForest), and synthetic minority over-sampling technique (SMOTE) were used to handle missing values, outliers, and imbalance in the dataset, respectively. The performance of the proposed models was evaluated using five metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The experimental results indicate that the BBO-DeepForest model performed best on the independent test set, with accuracy, precision, recall, F1-score, and average AUC values of 0.878, 0.682, 0.678, 0.678, and 0.961, respectively. A comparison with seven well-known imputation algorithms revealed the superiority of the selected imputation algorithm in recovering incomplete rock mass quality datasets. Model interpretation results showed that RQD and UCS are critical feature parameters for classifying slope rock mass quality. At last, the proposed BBO-DeepForest model was employed to verify the rock mass quality of three slopes at the Luming molybdenum mine, resulting in classifications consistent with on-site observations. It demonstrates that combining DeepForest with metaheuristic optimization algorithms is a feasible and accurate approach for intelligently classifying the rock mass quality of slopes.
