Search Results (157)

Traditional rock mass evaluation methods have three main limitations: (1) their application depends largely on specialist judgment; (2) their discrete classification approach, such as RMR (Rock Mass Rating) and SMR (Slope Mass Rating), leads to abrupt transitions between categories; and (3) the interaction among geomechancial parameters is limited; these aspects reduce their ability to represent slope behavior in a gradual manner. The main objective of this research was to develop a model capable of representing gradual transitions between geomechanical conditions and the interaction among parameters related to susceptibility to instability. The model uses a hierarchical modular framework based on the Mamdani fuzzy inference mechanism, allowing the incorporation of expert knowledge through linguistic rules. It is implemented in a graphical environment that allows users to directly use geomechanical parameters obtained through conventional characterization or from three-dimensional digital models derived from UAV (Unmanned Aerial Vehicle) photogrammetry. Model consistency was evaluated through a sensitivity analysis, which verified the model’s response coherence across variations in input parameters. The graphical evaluation tool was then applied to three real case studies with different geomechanical configurations, and the results were compared with those from traditional methods (RMR and SMR). The results showed differences between traditional and fuzzy approaches, as our proposal links recommendations to specific geomechanical conditions across different evaluation levels, identifying conditions for potential intervention measures. In addition, the model enables the zonification of instability susceptibility, facilitating its use in future risk analyses. Our model is intended for application under normal slope conditions, without accounting for extreme events or external dynamic loads, such as seismic activity, groundwater level variations, infiltration, or high-mountain conditions. Full article

During underground mining, stope stability is affected by excavation-induced stress redistribution and nonlinear degradation of rock mass strength. The conventional Mathews stability graph method employs an empirical stress factor A, which does not explicitly consider the nonlinear relationship between stress conditions and rock mass strength. In this study, the generalized Hoek–Brown criterion was introduced to define the maximum stress factor (MSF), and a modified stress factor A′ was developed by considering tensile and shear failure mechanisms. The proposed method incorporates the nonlinear stress–stability relationship of underground stopes and improves the reliability of stability assessment. A copper mine in southwest China was selected as a case study. The rock mass quality indices and stress parameters of ten representative stopes were obtained through field investigations, stope roof stress measurements, discontinuity surveys, and laboratory tests. The modified stress factor A′ was incorporated into the Mathews stability graph to account for excavation-induced stress redistribution and rock mass strength degradation. Compared with the conventional method, the modified approach generally reduced the stability numbers of the investigated stopes, with an average reduction of approximately 25.6% (excluding D1780-1, where the confinement strengthening effect resulted in a slight increase in stability number). The revised stability classifications show good consistency with the FLAC^3D simulation results and field observations, providing supporting evidence for the application of the proposed method to underground stope stability assessment in the studied mine. Full article

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. Full article

As mineral resource development moves deeper into the earth, mine dynamic disasters, represented by rockbursts, occur frequently. Due to the combined effects of in situ rock stress state and geostress conditions, it is difficult to obtain reliable prediction results using traditional empirical criteria or single-index prediction methods. To address these issues, this paper constructs a rockburst sample database based on the existing literature, including maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, elastic energy index, stress coefficient, and brittleness coefficient. Secondly, six typical machine learning algorithms are selected for rockburst level classification research. Then, to address the problem of imbalanced sample distribution, the SMOTE oversampling method is introduced to balance the data, and Bayesian optimization and cross-validation are combined to optimize the model hyperparameters. The results show that optimized XGBoost models exhibit high accuracy and stability in rockburst level discrimination, and accuracy reached 0.7664, recall was 0.7664, macro-P was 0.7664, and macro-F1 was 0.7662. Furthermore, taking the BO-XGBoost model as an example, the SHAP method is introduced to analyze the interpretability of the model’s prediction results. The results show that the elastic energy index and stress-related indices are the main controlling factors affecting the intensity of rockburst; they accounted for 25.75% and 22.68%, respectively. Based on the above research results, this paper further explores the ideas for rockburst safety management and prevention from the aspects of energy control, stress regulation, and optimization of rock mass structural characteristics, providing theoretical basis and technical support for the scientific formulation of rockburst risk identification, level prediction, and safety prevention and control measures in deep underground engineering. Full article

Reliable roof-water-inrush risk zoning in coal mines remains difficult under small-sample conditions because geological, hydrogeological, and mining-induced factors interact nonlinearly, and field labels are often derived from engineering evidence rather than complete accident records. This study aims to develop and preliminarily validate a deployment-oriented workflow for case-specific roof-water-inrush risk-zone classification in the No. 4⁻³ coal seam of Shiyangou Coal Mine, Shaanxi Province, China. A total of 100 spatial evaluation samples were compiled using eight indicators grouped into water-resisting capacity, water-supply intensity, water-conducting pathways, mining-induced disturbance, and rock-mass integrity. The Safe-Zone and Risk-Zone labels were generated from mine-water-prevention documents, borehole and hydrogeological evidence, abnormal goaf-water information, and expert engineering judgment. A lightweight CNN-LSTM model was trained with leakage-controlled data splitting, early stopping, L2 regularization, gradient clipping, and model-capacity control. Because the inputs are static or quasi-static spatial indicators, LSTM was used only as a gated feature-dependency module for CNN-derived feature sequences, and its necessity was examined using ablation and feature-order perturbation tests. In repeated stratified five-fold cross-validation, the model achieved mean accuracy, F1-score, and AUC values of 0.792 ± 0.108, 0.633 ± 0.111, and 0.829 ± 0.097, respectively. On an independent prediction dataset, the accuracy and AUC were 0.750 and 0.743, respectively. After ONNX export, STM32CubeMX evaluation indicated 68,962 B Flash and 3252 B RAM consumption. These results indicate the preliminary feasibility of embedded roof-water-inrush risk-zone classification for this specific mine case. However, the findings do not demonstrate cross-mine transferability; further validation using external datasets, independent labels, and field deployment records is required. Full article

Volcanic caves, commonly referred to as lava tubes, are typically shallow subsurface cavities formed by the cooling of a generally basaltic lava flow under a roof or crust that cools faster and acts as a thermal insulator. These cavities can serve as tourist attractions, in which case their stability must be analyzed and ensured. Empirical rock mass classification systems, in this case we have applied the Q-index have been employed to evaluate the stability of underground excavations: mines and tunnels, including natural caves. We have identified that these approaches have limitations, particularly incorporating key geometric parameters such as roof thickness and cave length. In this study we have analyzed applicability of the Scaled Span Method (SSM) to volcanics caves. This method was originally developed for the stability assessment of crown pillar stability in shallow mines. We have developed a dataset of lava tubes (caves) located in the Canary Islands (Spain), the Galápagos Islands (Ecuador), and Jordan. In this research we have conducted geomechanical characterization using the Q-system, and also the Scaled Span to evaluate stability based on cave geometry and rock mass properties. The results indicate that, in general, the SSM yields more conservative stability estimates compared to the Q-system, particularly for shallow caves with limited roof thickness. Nevertheless, discrepancies between the two approaches are observed in several cases, highlighting the limitations of directly transferring empirical methods developed for mining excavations to natural cave systems. These differences underscore the need for careful interpretation and, where appropriate, complementary stability analyses. The Scaled Span Method is useful for preliminary assessment of volcanic cave stability, especially in scenarios where potential interaction with the ground surface is expected: buildings or roads on top. However, its application requires adaptation and critical evaluation due to the fundamental differences between engineered mining excavations and natural subsurface cavities. Full article

Reliable assessment of rockburst tendency is essential for maintaining the stability and operational safety of deep underground excavations. However, the complex coupling among stress conditions, lithological characteristics, and structural features of rock masses introduces significant uncertainty into rockburst prediction. Conventional evaluation approaches relying on individual indices frequently produce inconsistent classifications and are often insufficient to represent actual rockburst behavior. To address this issue, a hybrid evaluation framework integrating unascertained measure theory, cloud-based uncertainty analysis, and a game-theoretic weighting strategy was developed in this study. Four representative parameters, including the strain energy storage index (W_et), geostress index (S), rock quality designation (RQD), and rock mass integrity factor (K_v), were adopted to characterize the energy-storage capability, stress environment, and structural condition of the surrounding rock mass. The conventional unascertained measure approach was further enhanced using the normal cloud model to describe the uncertain mapping relationship between quantitative measurements and qualitative rockburst classifications. In addition, a combination weighting scheme incorporating AHP, entropy weight (EW), and CRITIC methods was established to improve the stability and rationality of index weighting. The developed framework was subsequently applied to a deep phosphate mine in China. The calculated comprehensive weights of the four evaluation parameters were 0.1982, 0.3446, 0.2173, and 0.2399, respectively, demonstrating that the stress-related parameter has the greatest influence on rockburst evaluation. The results indicate that the investigated rock masses generally exhibit moderate-to-strong rockburst tendency. The shallow and moderately deep zones exhibited relatively high rockburst potential, while the ultra-deep dolomite formations mainly showed a moderate tendency due to the development of joints and fractures, which weakened the integrity of the deep rock mass. The proposed framework provides an effective and practical approach for preliminary hazard assessment, rockburst risk zoning, and prevention strategy design in deep mining engineering. Full article

The progressive development of microscopic fractures within rock masses is a primary mechanism of macroscopic failure, threatening the structural integrity of rock engineering systems. In this paper, a novel deep learning model, Principal Component Analysis (PCA)-Visual Geometry Group 16 (VGG16), is developed to accurately identify spectrogram features associated with limestone fractures. In this architecture, a PCA-based convolution encoder is seamlessly integrated as a foundational preprocessing layer before feedforwarding into the deep neural network to execute linear feature purification. The model is first validated on standard image datasets comprising handwritten digits and facial images to evaluate classification performance. Subsequently, acoustic emission signals are acquired during triaxial compression tests on limestone specimens pretreated with cyclic acid–alkali exposure. The PCA-VGG16 framework is then employed to classify the corresponding acoustic spectrograms, and its performance is quantitatively compared with a conventional convolutional neural network (CNN) and the standard VGG16 model. The results indicate that the PCA-VGG16 model achieves classification accuracies that are 19.19% and 10.77% higher than the conventional CNN and standard VGG16 models, respectively. In terms of computational efficiency, the training time is reduced by 35.00% and 23.53% compared to CNN and VGG16. The superior classification performance of the proposed PCA-VGG16 model enables accurate identification of internal microscopic fracture characteristics in limestone. Furthermore, the integration of acoustic emission signals with deep learning models offers an effective approach for quantifying internal fracture levels and predicting the progressive failure of rocks. Full article

Hydropower Classification (HC) is a widely used rock mass classification method in tunneling construction projects. Predicting the HC of the surrounding rock is significant for selecting tunneling parameters and guaranteeing tunneling safety. To predict the HC of a tunnel excavated by TBM, a Spearman-Weighted Supervised Prototype Classifier (SW-SPC) is proposed. According to the target task, the Spearman’s correlations between TBM tunneling features and HCs are utilized as distance weights to guide the prototype optimization process, while an unweighted distance-based prototype optimization classifier serves as a baseline for performance comparison. To verify the proposed method, a total of 275 field samples with matched tunneling data and HCs were collected from a tunnel project located in Northeast China. Among them, 200 samples made up the training and the other 75 samples made up the testing set. The SW-SPC model achieved an accuracy of 82.7% and the precision and recall reached 84.6% and 81.7% in the single static test split. To rigorously evaluate the generalization of SW-SPC, a 5-fold cross-validation was implemented. It yielded a consistent global accuracy of 82.0%, with precision and recall ranges of 59.3–61.0% and 58.3–66.5%, respectively. These metrics demonstrate the model’s robust global performance while highlighting the localized sensitivity to class distribution variance inherent in field-measured tunneling data. Full article

We present a whole-rock geochemical dataset of late Variscan intrusive rocks and residual anatectic melts from the mid- and lower continental crust exposed in the Serre Massif of Calabria (southern Italy). A total of 74 samples were collected from the main plutonic units and from leucosomes of associated migmatitic metasediments. The composition of intrusive rocks varies from tonalites and quartz-diorites at deeper structural levels, to peraluminous granites at shallower levels. The dataset includes major, trace and rare earth element (REE) analyses obtained using X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The dataset integrates new and previously published geochemical data into a consistent and reusable format, including sample locations (WGS84), lithological classification and lithostratigraphic attribution. Sampling sites are also provided as a downloadable geospatial (.kmz) file for visualization in GIS platforms. The data are intended to support a wide range of applications, including studies on granitoid magmatism, water–rock interaction processes in crystalline aquifers and raw materials exploration. Therefore, the dataset represents a valuable resource for both fundamental and applied geoscientific research. Full article

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. Full article

Tunnel construction requires accurate and timely classification of surrounding rock masses to ensure safety and guide excavation. This research addresses the limitations of conventional methods and unimodal intelligent approaches by proposing a novel hybrid deep model, Random-Mamba, that integrates drilling parameters and digital images for enhanced classification performance. A dataset of 3361 synchronized samples was constructed, containing six drilling parameters, digital face images, and expert-classified rock mass grades. The model employs a dual-branch architecture: a Random Forest processes the drilling parameters, and a MambaVision network extracts visual features, with a multilayer perceptron performing the fusion. The proposed model achieved an overall accuracy of 92.12% and a macro-F1 score of 91.66%, outperforming the most comparable hybrid model by 2.61% in accuracy. It demonstrated particularly high precision in identifying Class III rock with an F_1-score of 93.2%. Ablation and comparative experiments confirmed its superiority over both single-modality models, such as SVM and ResNet, and other hybrid architectures, like Random-Swin. SHAP-based sensitivity analysis further revealed that feed speed was the most influential drilling parameter for classification. The effective fusion of complementary mechanical and visual data provides a robust and practical solution for real-time rock mass assessment in tunneling engineering. Full article

The extraction of fracture parameters and the classification of surrounding strata are crucial criteria for assessing the stability of a tunnel face. To overcome the limitations of conventional manual sketching, this paper proposes a tunnel face fracture identification, extraction, and surrounding strata classification technique based on deep learning technology. Based on the collection of on-site tunnel face images, we construct a comprehensive database comprising 20,000 dataset samples. By refining the conventional UNet deep learning network model and incorporating the channel and spatial attention modules (Convolutional Block Attention Module, CBAM), we achieve automated identification of fracture traces on the tunnel face, yielding remarkable recognition outcomes. Through training and testing the CBAM-UNet network model on this extensive database, we conduct a comparative analysis with alternative deep learning approaches and conventional edge detection algorithms. The results unequivocally demonstrate the exceptional performance of the CBAM-UNet model in fracture recognition. Subsequently, we conduct statistical analysis and grouping of the identified fractures, as well as calculate the integrity indices of the surrounding rock mass. This enables the expeditious assessment of the tunnel face’s surrounding rock grade. Full article

The design of near-surface crown pillars involves the interaction between excavation geometry, rock mass quality, and the in situ stress state. This study proposes a formulation that integrates numerical modeling and empirical criteria to construct a unified framework for stability interpretation. The Modeled Span index is introduced as a hybrid measure that incorporates geometric relationships and the pre-mining stress state, which, when compared with rock mass quality, allows the definition of a probabilistic stability boundary. The results show that the design tool enables the establishment of a consistent classification within the domain represented by the dataset. The Modeled Span chart complements traditional stability assessment approaches and is intended for application in conceptual and prefeasibility studies of crown pillars in Sublevel Open Stoping mining. Full article

Accurate classification of rock fracture modes is essential for understanding rock mass instability mechanisms. To address the limitation of traditional acoustic emission (AE) classification methods that treat a single AE signal as a single fracture event, overlooking its composite nature from multiple fracture events and leading to misclassification, this study proposes a novel rock fracture mode classification method based on AE spectral analysis. This study details the development framework, theoretical model, classification criteria, application process, and experimental validation of the new rock fracture mode classification method. Uniaxial compression tests on granite, marble, and limestone, along with rockburst simulation tests on granite, were conducted to validate the classification of fracture modes. In rockburst simulations, shear fracture signals accounted for 48% on average, composite signals 40%, and tensile signals 12%. The method effectively distinguishes multiple fracture events within a single AE signal, accurately classifies fracture modes, and elucidates the dynamic evolution of fracture modes during the rockburst precursor stage, offering significant advantages for rock fracture mode classification and mechanistic insight. Full article