Search Results (72)

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction. 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

Recent advances in deep learning and artificial intelligence (AI) have significantly transformed the analysis of rock slopes and geotechnical structures. Rock slope stability is governed by complex interactions among rock mass discontinuity networks, mechanical properties, environmental loading conditions, and stress redistribution. Traditional analytical and numerical methods, including discrete element methods, finite element simulations, and limit equilibrium approaches, provide valuable insights; however, they often have limitations in capturing complex failure mechanisms and handling heterogeneous datasets. This review systematically synthesizes recent developments in AI-driven approaches for rock slope engineering, with particular emphasis on their integration with physical and numerical modeling frameworks and their role in improving the performance assessment of geotechnical systems. Key applications include machine learning-based slope stability prediction, automated discontinuity detection, surrogate modeling for numerical simulations, and spatiotemporal forecasting of slope deformation using monitoring data. The review further discusses emerging approaches such as physics-informed machine learning, digital twin systems, and hybrid AI–numerical frameworks, which combine data-driven learning with established rock mechanics principles. In addition, the potential of AI technologies to support sustainable rock slope management is evaluated, including early warning systems, optimal stabilization design, and resilient infrastructure monitoring. Finally, major challenges related to data quality, model interpretability, uncertainty, and integration with physical models are identified. The review suggests that future research should focus on integrating AI with physics-based modeling and uncertainty quantification, supported by rigorous validation strategies and high-quality datasets, to improve reliability and practical applicability in rock slope engineering. This paper provides a comprehensive perspective on how AI and deep learning can improve the understanding, prediction, and long-term management of rock slopes in modern geotechnical engineering practice. Full article

Blasting-induced ground vibrations, typically quantified by peak particle velocity (PPV), pose one of the most critical environmental challenges in surface mining and can damage nearby structures and disrupt surrounding ecosystems. Consequently, the development of reliable and accurate predictive models is essential for designing safe, environmentally responsible, and sustainable blasting operations. This study develops a robust predictive framework using a harmonized database of 506 blasting events, from which 386 high-quality records were retained after preprocessing to model PPV as a function of charge per delay (Q), monitoring distance (R), and rock mass rating (RMR). Several machine learning (ML) algorithms, including artificial neural networks trained using the Levenberg–Marquardt algorithm (ANN-LM), adaptive neuro-fuzzy inference systems (ANFIS), Gaussian process regression (GPR), and decision trees (DT), were evaluated alongside conventional empirical models such as the USBM, Ambraseys–Hendron, Langefors–Kihlstrom, and BIS. To further enhance predictive capability, two optimization strategies, Bayesian optimization (BO) and differential evolution (DE), were applied to the GPR model, producing optimized BO-GPR and DE-GPR variants. Model performance was assessed using the correlation coefficient (r), variance accounted for (VAF), mean absolute error (MAE), and relative root mean square error (RRMSE). Results indicate that the BO-GPR model achieved the best predictive performance during testing for both the two-input (Q, R) and three-input (Q, R, RMR) configurations, with r values of 0.97426 and 0.98381, respectively, and VAF values exceeding 94%. SHAP analysis revealed monitoring distance as the dominant attenuating factor controlling PPV. The optimized framework provides an accurate, interpretable tool for vibration prediction and precision blast design, supporting environmentally responsible, sustainable mining operations. Full article

Limestone quarrying relies strongly on drilling and blasting, processes whose performance depends on both the geomechanical conditions of the rock mass and the resulting fragmentation. This study integrates rock mass characterization, blast analysis, and slope-stability assessment to optimize rock breakage and operational safety. Rock mass quality was evaluated using RMR, GSI, RQD, A-factor, and SMR, while slope stability was analyzed through the limit equilibrium method and kinematic analysis. Fragmentation was quantified using UAV-based photogrammetry combined with AI-driven particle-size detection, enabling the construction of granulometric curves. These data were incorporated into the Kuz–Ram model, applying the Ash method to determine optimal drilling patterns. Five distinct rock masses were identified, and wedge- and block-type instabilities were detected along the working face. Mitigation measures, including catch berms and drainage ditches, were proposed. Optimal burden values ranged from 2.59 to 3.35 m, yielding D₈₀ values below 60 cm. Full article

Mineral resources serve as a critical foundation for China’s energy system, with the Ordos Basin’s Tarangole mining area being a key mineral production base in the central and western regions. To support the restoration, development, and productivity enhancement of the mining area, this research systematically investigates the geological and mechanical properties of the sandstone in the region. Herein the innovation lies in its comprehensive analysis of the influence mechanisms of multiple factors—such as geological groups, particle size, evaluation indicators, sampling depth, temperature, and creep rate—on the mechanical behavior of sandstone. The study, through engineering geological surveys and mechanical testing of frozen sandstone (including uniaxial and triaxial creep tests), led to the following key findings: (1) the sandstone in the area is prone to softening and disintegration, classified as soft to moderately soft rock (UCS range: 5.14–10.26 MPa in natural state), with a basic quality grade of IV–V. (2) The thermal conductivity and specific heat capacity of the rock vary significantly with temperature. The recommended freezing temperature is −5 °C, based on engineering experience and economic considerations. (3) Freezing can effectively enhance the strength of sandstone (e.g., the strength of medium- and coarse-grained sandstone increases by 5 MPa at −20 °C compared to −10 °C), although it still falls within the category of extremely soft rock. (4) The water-ice phase transition induced by low temperatures significantly enhances the overall strength, stiffness, and deformation resistance of saturated sandstone. Accordingly, freezing measures can effectively enhance rock mass strength under low-temperature conditions. It is recommended that mining operations be prioritized during winter or colder seasons to ensure construction safety and efficiency. Full article

Against the background of global energy transformation and low-carbon development, numerous difficult-to-mine coal resources (e.g., deep, thin coal seams and low-quality coal) remain underdeveloped, leading to potential resource waste. This study systematically summarizes the feasibility of developing these resources via underground coal gasification (UCG) technology, clarifies its basic chemical/physical processes and typical gas supply/gas withdrawal arrangements, and establishes an analytical framework covering resource utilization, gas production quality control, environmental impact, and cost efficiency. Comparative evaluations are conducted among UCG, surface coal gasification (SCG), natural gas conversion, and electrolysis-based hydrogen production. Results show that UCG exhibits significant advantages: wide resource adaptability (recovering over 60% of difficult-to-mine coal resources), better environmental performance than traditional coal mining and SCG (e.g., less surface disturbance, 50% solid waste reduction), and obvious economic benefits (total capital investment without CCS is 65–82% of SCG, and hydrogen production cost ranges from 0.1 to 0.14 USD/m³, significantly lower than SCG’s 0.23–0.27 USD/m³). However, UCG faces challenges, including environmental risks (groundwater pollution by heavy metals, syngas leakage), geological risks (ground subsidence, rock mass strength reduction), and technical bottlenecks (difficult ignition control, unstable large-scale production). Combined with carbon capture and storage (CCS) technology, UCG can reduce carbon emissions, but CCS only mitigates carbon impact rather than reversing it. UCG provides a large-scale, stable, and economical path for the efficient clean development of difficult-to-mine coal resources, contributing to global energy structure transformation and low-carbon development. Full article

Ion adsorption rare-earth (IARE) ores, a strategic metal resource, are extracted by leaching with ammonium sulfate [(NH4)₂SO₄] solution, our samples have ∑REO grades of 0.032–0.079% wt%. IARE sandstone, mudstone, clay, and strongly weathered rock were selected as test materials. Surface-related physicochemical parameters were determined, and bound water was determined by volumetric flask pycnometry. For each IARE lithology, we also obtained particle size distributions and evaluated bound water variation in (NH₄)₂SO₄ solutions at 0, 1, 2, and 3 wt%. Based on the Gouy–Chapman theory, the relationship between the surface bound water and solution concentration, as well as the surface charge of IARE samples, and other influencing factors was explored. The experimental results show the following: ① The surface charge per unit area of four types of IARE samples, namely mudstone, sandstone, clay, and strongly weathered rock, are 0.7072 × 10⁻² mmol/m², 1.9620 × 10⁻² mmol/m², 1.5418 × 10⁻² mmol/m², and 2.1003 × 10⁻² mmol/m², respectively, with strongly weathered rock having the highest and mudstone having the lowest. ② As the concentration of aqueous (NH₄)₂SO₄ increases (0, 1, 2, 3 wt%), the total volume reduction in free water ∆V in the system increases, and the mass of adsorbed bound water per unit mass of IARE sample also increases. ③ As the concentration of the solution increases, the thickness of the diffusion double layer on the surface of the IARE sample is compressed, the total amount of adsorbed anions and cations on the surface increases, and the density of the surface water film also increases, leading to a corresponding increase in the quality of adsorbed bound water. ④ Under the same solution concentration, the variation trend of adsorbed bound water mass per unit area of IARE samples is strongly weathered rock > sandstone > clay > mudstone, which is consistent with the trend of surface charge per unit area of IARE samples. A higher lixiviant concentration increases bound water, shrinks the effective pore throats of the ore body, reduces hydraulic conductivity, and consequently diminishes leaching efficiency. Full article

This study takes the Wushan open-pit mine, a typical open-pit mine in cold regions, as the engineering background. Based on the measured extreme temperature values of slope rock masses over one year, a freeze–thaw cycle testing scheme is designed. By conducting experiments under varying numbers of freeze–thaw cycles and burial depths, the degradation patterns of uniaxial compressive strength and tensile strength of the rock are revealed. The rock material constant $\mathrm{m}\mathrm{i}$, representing the rock’s hardness and brittleness, is calculated based on the experimental results. Furthermore, shear tests are carried out on rock masses containing through-going structural planes and infill materials to derive the variation patterns of cohesion and internal friction angle. A comprehensive analysis is conducted on the effects of freeze–thaw cycling and burial depth on rock mechanical properties and infill material parameters, leading to the construction of a spatial variability characterization model for mechanical parameters. Finally, the rock mass fracture coefficient ${\mathrm{K}}_{\mathrm{w}}$ and infill fracture coefficient $K_f$ are proposed to modify the Hoek–Brown failure criterion under freeze–thaw conditions, thereby providing theoretical support for slope stability analysis and engineering design in cold regions. Full article

The spatial variability in rock mass mechanical parameters critically affects slope stability assessments. This study investigated the southern slope of the Bayan Obo open-pit mine. A representative elementary volume (REV) with a side length of 14 m was determined through discrete fracture network (DFN) simulations. Based on the rock quality designation (RQD) data from 40 boreholes, a three-dimensional spatial distribution model of the RQD was constructed using Ordinary Kriging interpolation. The RQD values were converted into geological strength index (GSI) values through an empirical correlation, and the generalized Hoek–Brown criterion was applied to develop a spatially heterogeneous equivalent mechanical parameter field. Numerical simulations were performed using FLAC3D, with the slope stability evaluated using the point safety factor (PSF) method. For comparison, three homogeneous benchmark models based on the 5th, 25th, and 50th percentiles produced profile-scale safety factors of 0.96–1.92 and failed to replicate the observed failure geometry. By contrast, the heterogeneous model yielded safety factors of approximately 1.03–1.08 and accurately reproduced the mapped sliding surface. These findings demonstrate that incorporating spatial heterogeneity significantly improves the accuracy of slope stability assessments, providing a robust theoretical basis for targeted monitoring and reinforcement design. Full article

The structure from motion (SfM) and multiview stereo (MVS) techniques have proven effective in generating high-quality 3D point clouds, particularly when integrated with unmanned aerial vehicles (UAVs). However, the impact of image quality—a critical factor for SfM–MVS techniques—has received limited attention. This study proposes a method for optimizing camera settings and UAV flight methods to minimize point cloud errors under illumination and time constraints. The effectiveness of the optimized settings was validated by comparing point clouds generated under these conditions with those obtained using arbitrary settings. The evaluation involved measuring point-to-point error levels for an indoor target and analyzing the standard deviation of cloud-to-mesh (C2M) and multiscale model-to-model cloud comparison (M3C2) distances across six joint planes of a rock mass outcrop in Seoul, Republic of Korea. The results showed that optimal settings improved accuracy without requiring additional lighting or extended survey time. Furthermore, we assessed the performance of SfM–MVS under optimized settings in an underground tunnel in Yeoju-si, Republic of Korea, comparing the resulting 3D models with those generated using Light Detection and Ranging (LiDAR). Despite challenging lighting conditions and time constraints, the results suggest that SfM–MVS with optimized settings has the potential to produce 3D models with higher accuracy and resolution at a lower cost than LiDAR in such environments. Full article

Laocong Shuixian (LCSX), a premium Wuyi rock tea derived from aged Shuixian tea trees, is valued by consumers for its distinctive “Cong flavor”—a unique aroma profile characterized by woody, bamboo leaf, and glutinous rice notes. However, the chemical basis and underlying mechanisms of this unique aroma remain unclear. Here, we assessed and established a professional sensory evaluation panel using the PanelCheck software, with significant F-value levels >5% confirming the panel’s discriminative capacity for key “Cong flavor” attributes. Combining a literature review and sensory analysis, we identified the descriptive terms associated with the “Cong flavor” of LCSX. Gas chromatography–olfactometry–mass spectrometry (GC–O–MS) analysis revealed 36 key aroma-active compounds, among which theaspirone (OAV = 500.05, ACI = 37%, R_woody = 0.82), δ-decalactone (OAV = 65.6, ACI = 4.3%, R_woody = 0.77), and 2-acetylpyrrole (OAV = 163, ACI = 9%, R_rice = 0.74) were identified as the contributors to the woody and rice-like notes of LCSX based on odor activity values and correlation analyses. Molecular docking results demonstrated that these compounds spontaneously bind to multiple olfactory receptors, with binding affinity ≤−5.0 kcal/mol, providing insights into their roles in human aroma perception: theaspirone to OR8D1; δ-decalactone to OR1E2, OR5M3, OR7D4, OR7G1, OR8D1 and OR8G1; and 2-acetylpyrrole to OR1E2, OR1G1, OR5M3, OR7D4, OR7G1, OR8D1, and OR8G1. This study enhances our understanding of the formation of distinctive aroma qualities in oolong tea and establishes a foundation for further research into its sensory and chemical properties. Full article

The accurate evaluation of rock mass quality and competent roadway-support decision-making requires the rapid and accurate acquisition of the distribution of structural planes in rocks. To address this need, a program was developed that uses deep learning to automatically recognize the structural plane in-borehole images. First, borehole images from 30 mines in China were collected during field tests, and the structural planes in the images were categorized into five types. Second, a deep Coral architecture based on a convolutional neural network (CNN) was established to automatically extract features from the borehole images and classify the structural planes therein. The experimental results indicate that the CNN model classifies the structural planes in the borehole images with an overall accuracy of 86%. Validation tests in field applications demonstrated recognition accuracies ranging from 0.76 to 1.0 compared to manual markings, meeting engineering requirements. Finally, based on the proposed method, an intelligent system to recognize surrounding rock fracture was developed. Engineering application cases are presented and discussed to demonstrate the method and confirm the accuracy of this approach. Compared with traditional classification methods, the proposed method rapidly recognizes and classifies structural planes in borehole images at low cost, with precision, and in a non-destructive and automated manner. Full article

Metallic ore deposits are generally formed through magmatic intrusions, followed by metamorphism. The geological structures in such regions are often complex, with mechanical parameters exhibiting significant variability. These characteristics dictate the need for refined geological modeling and heterogeneous mechanical parameters for rock mass stability analysis to ensure reliability. Therefore, this paper proposes a novel method for rock mass stability analysis. The method fully leverages high-density drilling data from the mine and introduces an intelligent rock quality designation (RQD) identification technique, facilitating characterization of the spatial heterogeneity of rock mass RQD. Building on this, laboratory experiment data and in situ measurements are integrated, and the Hoek–Brown criterion is employed to achieve a refined characterization of heterogeneous rock mass mechanical parameters. This method allows for a realistic inversion of in situ rock mass mechanical conditions, overcoming the limitations inherent in assigning uniform parameters. Finally, the computed rock mass mechanical parameters are assigned to the refined computational model to conduct rock mass stability analysis. Taking the Jiangfeng Iron Mine, with its complex geological conditions, as an example, this method enables the accurate evaluation of the rock mass stability, determines the feasibility of joint mining, and calculates the appropriate thickness of the isolation pillars, effectively mitigating safety risks in mining operations. This method provides a valuable reference for the rock mass stability analysis of underground joint mining operations for similar mines. Full article

Wuyi Rock tea (WRT), originating from the northern region of Fujian province, has a good reputation for its distinctive Yan flavor and floral–fruity aroma. The aroma quality, an essential element of the Yan flavor, undergoes various changes during the manufacturing process of WRT. To enhance the understanding of the formation patterns of WRT aroma and its influence on the flavor quality of WRT, we utilized both manufactured WRT (Rougui tea) and primary tea as materials. Utilizing a sensory evaluation, detection of volatile compounds, and multivariate statistical analysis, we identified and characterized the distinctive volatile components present in WRT. The sensory evaluation and radar chart analysis revealed that the primary tea exhibited a strong and lasting aroma, along with a mellow taste and a prominent Yan flavor. Through gas chromatography time-of-flight mass spectrometry (GC-TOF MS), a total of 251 volatile compounds were identified. The odor activity value (OAV) was calculated to identify key aroma-active compounds in the primary tea. The results indicated that a total of 14 compounds had an OAV greater than 1.0, including (2-nitroethyl) benzene, indole, and geranylacetone. These compounds exhibited floral and fruity aroma attributes. They primarily formed and accumulated during the latter stages of WRT. Using a partial least squares discrimination analysis (PLS-DA) combined with a variable importance in projection (VIP) score greater than 1.0 as a criterion, a total of 89 compounds were identified. Furthermore, out of the selected compounds, 15 types, including geraniol, 1-nonanol, and 1-butyl-2-ethyl-cyclopropene, were found to exclusively exist during the enzymatic manufacturing stages, particularly during the intermediate and later phases of the turn-over process (the last-three-times turn-over treatments), exhibiting predominantly floral and sweet fragrances. In contrast, during the non-enzymatic stages, only four compounds, such as pentanoic acid and phenylmethyl ester, were detected, exhibiting a fruity aroma profile. These volatile compounds significantly influenced the quality attributes of the final tea product, resulting in strong and lasting characteristics, particularly marked by a pronounced floral and fruity aroma. This study revealed how the aroma quality in WRT is developed and pinpointed possible volatile compounds that react to post-harvest treatments, thereby offering valuable insights relating to the intelligent production strategies of WRT. Full article