Search Results (1,538)

Conventional drilling and coring methods are inherently limited to providing one-dimensional geological data, which hinders accurate characterization of the spatial distribution of rock mass structures and properties. Mechanical disturbances during drilling often cause core breakage, further compromising the fidelity of in situ geological representation. This study proposes an integrated approach combining borehole optical imaging and GPR to enhance the characterization of rock mass structures. A dynamic exploration method is introduced, defined as an adaptive drilling layout workflow based on phased information feedback. The fundamental concept, key assumptions, boundary conditions, and field implementation procedures of this dynamic survey are systematically described. The integrated method is applied to a high-speed railway investigation project in the Tengzhou section, Shandong Province, China, where six boreholes were surveyed using both techniques. Results demonstrate that fused analysis of borehole optical images and GPR data effectively reveals rock morphology, fracture distribution, joint systems, and fractured zones. Optical imaging provides high-resolution orientation data at the borehole wall. Borehole GPR extends detection radially into the surrounding rock mass. Together, the two methods enable spatially enhanced characterization and partially mitigate the azimuthal ambiguity inherent in single-borehole radar measurements. A triangular borehole survey scheme is shown to be feasible for locating subsurface anomalies. The proposed method effectively reduces borehole requirements compared to conventional grid layouts. Through the integrated analysis of optical imaging and GPR data, common anomalous features can be successfully identified. The method demonstrates practical applicability for detecting fractures with apertures greater than 1 cm and meter-scale cavities. Good consistency between the two techniques validates the feasibility of this integrated approach. The method’s limitations, including resolution constraints and detection omission risks, are explicitly acknowledged, and risk control strategies are proposed. Overall, the dynamic exploration approach reduces investigation costs and accelerates project timelines. It also provides a practical framework for the spatial characterization of rock mass discontinuities with minimal borehole requirements. Full article

Coal masses with well-developed cleats exhibit pronounced heterogeneity and anisotropy, and obtaining intact cores for mechanical testing remains a persistent challenge in engineering practice. Conventional assessments using the Hoek-Brown (HB) criterion rely heavily on empirical geological indices and cannot establish a quantitative correlation between cleat characteristics and rock mass parameters, thereby leading to low accuracy and efficiency in strength evaluation. In this study, numerical coal models are established using the discrete element method (DEM) combined with laboratory mechanical tests, and a series of uniaxial and triaxial compression simulations are conducted. Results reveal that cleat intensity is negatively correlated with uniaxial compressive strength and peak strain, while matrix stiffness and intermediate principal stress positively affect the elastic modulus and strength of coal; the intrinsic mechanical parameters of cleats exert a limited influence on the macroscopic mechanical behavior. A linear correlation between 2D cleat areal density P21 and 3D intensity P32 is verified, and a prediction model for HB parameters m and s based on cleat features is developed. The proposed method only requires profile cleat statistics and a limited number of uniaxial tests to achieve efficient and reliable strength evaluation. It possesses considerable theoretical innovation and practical engineering value. Full article

This study investigates Cambrian and Sinian carbonate outcrops in the Tarim Basin using 19 stratigraphically diverse rock samples. Through integrated X-ray diffraction mineralogical analysis, triaxial compression testing, and Brazilian splitting experiments, we systematically characterized rock mechanical properties and their correlations with microscopic mineral constituents. Key findings demonstrate remarkably distinct mechanical properties across formations: vuggy dolomites from the Xiaqiulitage formation exhibit the lowest compressive strength (minimum 200.0 MPa) and tensile strength (3.85 MPa), while the Yuertusi formation’s Y5 layer dolomites achieve exceptional tensile strength (21.69 MPa). Mineral composition fundamentally controls rock strength: dolomite or quartz concentrations exceeding 90% significantly enhance strength, whereas calcareous minerals (calcite, fluorapatite) degrade mechanical integrity. Most specimens display pronounced brittle failure characteristics; uniquely, basal dolostones of the Awatage formation exhibit distinctive plastic deformation. This research elucidates the synergistic effects of tectonic history, mineral assemblages, and microtextural attributes on rock mechanical behavior, providing critical theoretical underpinnings for deep carbonate reservoir development in overpressured basins. Full article

Deep coalbed methane (CBM) is a core exploration and development domain for increasing the reserves and production of unconventional natural gas in China. A systematic understanding has been established on the enrichment and accumulation mechanism of high-rank deep CBM in the southern section of the eastern margin of the Ordos Basin. However, the medium-rank deep CBM in the Mugua Area of the Shenfu Gas Field in the northern section of the eastern margin has essential differences from that in the southern section in terms of coal rank and hydrocarbon generation–occurrence mechanism, and its accumulation and enrichment regularity remain unclear. The core innovations of this study are as follows: aiming at the unclear accumulation regularity of medium-rank deep CBM in the northern section of the eastern margin of the Ordos Basin, we first reveal the spatiotemporal synergistic coupling reservoir-controlling mechanism of five factors (sedimentation–thermal evolution–temperature–pressure–preservation), determine the 1750 m critical transition zone of the deep CBM occurrence state, and establish two types of accumulation models adapted to the geological characteristics of medium-rank coal. Taking the No.8+9 coal seams of the Taiyuan Formation in the Mugua Area as the research object, based on the theoretical foundation of the dual properties of coal seams as the “source rock–reservoir”, this paper comprehensively adopted technical means such as core observation, drilling and logging data, and high-pressure isothermal adsorption experiments to carry out systematic multi-dimensional studies on sedimentary microfacies, coal reservoir characteristics, thermal evolution degree, and gas-bearing property; identified the main controlling factors of CBM accumulation; and constructed the accumulation model. The results show the following: ① The main burial depth of the coal seams is more than 1700 m, with a thickness ranging from 7.0 to 21.3 m and an average of 15.1 m, and the coal structure is dominated by the primary structure; maximum vitrinite reflectance (R_o,max) is generally distributed from 0.90% to 1.39% with an average of 1.08%, belonging to typical medium-rank coal; and the organic matter type is mainly Type III, with an average gas content of 10.01 m³/t, where the average proportion of desorbed gas in the total gas content is 83.91%, featuring superior source and reservoir conditions and a good foundation for CBM enrichment. ② The CBM accumulation in this area is jointly controlled by the coupling of four factors: sedimentation, thermal evolution degree, temperature–pressure effect, and preservation conditions. The tidal flat–lagoon facies control the development of high-quality coal seams; regional metamorphism dominates the hydrocarbon generation capacity and gas quality of coal seams; the temperature–pressure coupling forms a critical adsorption zone at 1750 m, defining the differentiation boundary of the occurrence state of deep CBM; and high-quality mudstone cap rocks, a stable structural environment, and closed hydrodynamic conditions constitute the three key guarantees for gas enrichment. ③ Two types of accumulation models are divided: “source–reservoir integration + multi-factor synergistic enrichment type” and “source–reservoir limited + insufficient accumulation condition type”. Among them, the four reservoir-controlling factors of the synergistic enrichment type are highly coupled, with excellent gas-bearing property and strong recoverability. This study systematically clarifies the enrichment and accumulation regularity of medium-rank deep CBM in the Mugua Area and improves the accumulation theory of medium-rank deep CBM in the northern section of the eastern margin of the Ordos Basin. Full article

Calycosin-7-O-β-D-glucoside (CG), a bioactive compound extracted from the traditional Chinese herb Astragalus (AR), exhibits diverse biological activities, including anti-oxidative and anti-inflammatory effects, and has shown protective properties in ischemia–reperfusion (I/R) injury. While previous studies have demonstrated that CG mitigates I/R injury primarily through its anti-oxidative and anti-inflammatory actions, its potential role in promoting neuroregeneration—a critical process for stroke recovery—remains unclear, and the underlying mechanisms have yet to be elucidated. In this study, an ischemic stroke model was established in rats via middle cerebral artery occlusion (MCAO). Seven days after CG treatment, cerebral infarct volume was assessed using triphenyltetrazolium chloride (TTC) staining, while neurological function was evaluated through behavioral tests. Nissl staining and Bielschowsky silver staining were employed to examine neuronal damage and axonal loss, and immunofluorescence was used to assess axonal regeneration. The expression of key proteins in the Rho/ROCK signaling pathway was analyzed by Western blotting (WB) and quantitative real-time PCR (qRT-PCR). CG treatment significantly reduced infarct volume, promoted axonal regeneration, improved neurological outcomes, and modulated the expression of RGMa, Rho, ROCK, and CRMP2. Collectively, these findings provide the first evidence that CG facilitates axonal regeneration and neurological recovery after cerebral ischemia, at least in part by inhibiting activation of the Rho/ROCK pathway, highlighting its potential as a therapeutic agent for ischemic stroke. Full article

Unconventional oil and gas reservoirs naturally have low porosity and low permeability, which necessitate reservoir stimulation during production to achieve commercial exploitation. Therefore, to improve reservoir stimulation effectiveness, this study established a thermal–hydraulic–mechanical coupled numerical model suitable for hydraulic fracturing experiment scales based on rock mechanics, elasticity mechanics, damage mechanics, and flow mechanics theories, combined with maximum principal stress and Mohr–Coulomb damage criteria. The model was numerically solved within a finite element framework and used to simulate the reservoir hydraulic fracturing process. The results indicate that the propagation behavior of hydraulic fractures is controlled by reservoir rock mechanical properties, geostresses, reservoir temperatures, fracturing fluid viscosities, and injection rates. Among these, the increase in principal stress difference, reservoir temperature, fracturing fluid viscosity and injection rate promotes the propagation of hydraulic fractures along the direction of the maximum horizontal principal stress, whereas an increase in the rock’s elastic modulus reduces the propagation length of the hydraulic fractures. During fracturing, the fracturing fluid fractures the reservoir rock, significantly improving its porosity and permeability. This not only enhances the mobilization of unconventional oil and gas resources but also provides effective flow pathways for their migration, thereby ensuring the commercial viability of unconventional oil and gas resource extraction. Additionally, selecting a fracturing process that matches the geological characteristics of the study area during fracturing design is a prerequisite for improving the reservoir stimulation effect. The results of this study provide a reference for fracturing design and optimization. Full article

Understanding the anisotropy in the physical and mechanical properties of layered rocks is essential for predicting and preventing instability in layered rock masses. However, in-situ sampling is often hindered by the difficulty of obtaining specimens with controlled bedding orientations. Cement-based 3D printing (3DP) offers an efficient approach for fabricating rock analogues, yet the inherent anisotropy induced by the layer-by-layer deposition process has not been well characterized, hindering its broader application. The objectives of this study are (i) to systematically evaluate the intrinsic anisotropy of cement-based 3DP rocks and (ii) to compare the mechanical anisotropy and failure modes of 3DP layered rocks with those of natural layered sandstone. The key findings are as follows: (1) The uniaxial compressive strength (UCS), P-wave velocity, and computed tomography (CT) number of the 3DP rock vary by less than 6% among the X-, Y-, and Z-directions, indicating lower intrinsic anisotropy compared to typical sandstones and several other natural rocks. (2) The UCS, elastic modulus, and secant modulus of the 3DP layered rocks all decrease initially and then increase with bedding dip angle, reaching a minimum at 60°. (3) The main fracture characteristics of the 3DP layered rocks are similar to those of layered sandstone; notably, the 3DP layered soft rock exhibits the most pronounced shear failure features. This study quantifies the low intrinsic anisotropy of cement-based 3DP rocks and validates their similarity to natural layered sandstone in both mechanical anisotropy and failure modes. It thereby provides a reliable, reproducible basis for physical modeling of layered rock masses using 3DP, offering a new approach for laboratory-scale investigations of layered rocks. Full article

The accurate and efficient acquisition of rock mechanical properties is critical for ensuring the safety and efficiency of underground engineering construction. Traditional laboratory tests are characterized by long cycles, high costs, and an inability to reflect in situ mechanical properties, while existing deep learning models based on while-drilling data suffer from poor noise robustness, insufficient deep feature extraction, and low accuracy in synchronous multi-parameter prediction. To address these limitations, this paper proposes a hybrid deep learning model (CNN-LSTM-MoE) combining a convolutional neural network (CNN), a long short-term memory network (LSTM), and a mixture of experts (MoE) system. The model enables intelligent prediction of elastic modulus, Poisson’s ratio, and yield stress from while-drilling parameters. The proposed model integrates CNN’s local feature extraction capability, LSTM’s temporal dependency modeling capability, and the multi-expert dynamic fusion mechanism of MoE. Furthermore, it incorporates physical constraints from rock fragmentation mechanics and an adaptive multi-objective loss weight optimization strategy to comprehensively enhance the multi-parameter synchronous prediction performance. Experimental results demonstrate that the proposed model achieves coefficients of determination (R²) of 0.8965 for elastic modulus, 0.9193 for Poisson’s ratio, and 0.9813 for yield stress on the laboratory validation dataset, with a mean squared error (mse) of 4.0720. Its prediction performance significantly outperforms benchmark models such as TCN and Transformer time-series architectures. Ablation studies further validate the critical role of the integrated LSTM and MoE modules in improving model accuracy, with the MoE module contributing an average R² improvement of approximately 24%. This study not only provides an effective method for high-precision acquisition of rock mechanical parameters while drilling, but also offers a feasible solution based on numerical simulation for data augmentation to address the common issue of scarce labeled data in deep learning applications within engineering fields. Full article

Against the backdrop of growing demand for environmentally friendly reinforcement in geotechnical engineering, natural fiber reinforcement combined with microbial-induced calcium carbonate (MICP) and enzyme-induced calcium carbonate (EICP) technologies has garnered significant attention due to their eco-friendly and efficient advantages. However, few studies have reported the combined application of these three techniques for sand consolidation. This study employs a combined MICP-EICP approach with natural fiber reinforcement to enhance the overall strength of sandy soils and investigate related rock fracture permeability phenomena. Tests conducted include calcium carbonate content, unconfined compressive strength, permeability coefficient, and permeability flow rate. Results indicate that when brown fiber length is 6 mm and dosage is 0.8%, the unconfined compressive strength of MICP-EICP composite specimens reaches a maximum of 0.61 MPa, calcium carbonate content peaks at 7.07%, and permeability coefficient drops to a minimum of 0.0044 cm/s. This composite method offers a highly promising and sustainable improvement solution for geotechnical engineering applications such as sand consolidation, crack sealing, and cultural relic restoration. It not only optimizes mechanical properties but also enhances the utilization rate of waste materials. Full article

Quartzites containing malachite–azurite mineralization identified in the Kırşehir region (Central Anatolia, Türkiye) are located within the Kırşehir Massif. This study aims to comprehensively characterize the mineralogical, petrographic, geochemical, and gemological properties of these quartzites and to evaluate their potential as ornamental and decorative stones. Due to their characteristic green and blue colors, malachite and azurite possess significant aesthetic value. Their occurrence within silica-rich and mechanically resistant quartzites enhances the visual appearance of the host rock while maintaining its structural integrity. Petrographic observations indicate that the quartzites exhibit a granoblastic texture composed of interlocking quartz crystals. Malachite–azurite mineralization is structurally controlled by NW–SE-trending fracture systems and occurs predominantly as fracture-filling and cavity-coating phases, indicating an epigenetic origin related to post-metamorphic fluid circulation. Geochemical analyses reveal that the samples are dominated by SiO₂ (~95.33 wt.%), with CuO (~1.77 wt.%) and elevated Cu contents (3205 ppm) confirming the presence of copper carbonate mineralization. Although the high silica content contributes to overall mechanical strength, the relatively low Mohs hardness (3–4) and chemical sensitivity of malachite and azurite represent important limitations. Quantitative gemological measurements further indicate that quartz-rich domains exhibit high hardness values (up to ~907 HL), whereas malachite–azurite-bearing zones display significantly lower hardness (~735 HL) due to fracture-controlled heterogeneity and the presence of softer carbonate phases. Surface brightness measurements show moderate to high gloss values (~73–80 GU), with noticeable improvement following epoxy impregnation. These results demonstrate that while the material exhibits favorable optical properties, its mechanical performance is strongly influenced by mineralogical contrasts and structural discontinuities. Quartzites bearing malachite–azurite mineralization are therefore suitable primarily for decorative stones, ornamental objects, and small-scale jewelry applications rather than high-quality gemstones. The striking color contrast between azurite, malachite, and quartz enhances their visual appeal, and with appropriate stabilization techniques, their durability and economic value can be partially improved. Full article

The commercial development of deep shale gas reservoirs depends mainly on hydraulic fracturing. Concurrently, the accurate prediction of rock mechanical parameters is critical to informed decision-making and the optimization of hydraulic fracturing parameters. Traditional methods for developing deep shale reservoirs primarily rely on empirical formulas based on compressional and shear-wave velocities. However, acquiring shear wave data is challenging, and the accuracy of such formulas is often low. Conventional machine learning algorithms exhibit limited predictive accuracy and generalization due to the significant heterogeneity of rock-mechanical data. To efficiently predict rock mechanical parameters with high precision in deep shale formations and to improve guidance for optimizing hydraulic fracturing designs in the deep shale reservoirs of Western Chongqing, this study integrates logging data with laboratory rock-mechanical test data. This research moves beyond simplistic model stacking and utilizes a customized architectural design tailored to the three core characteristics of deep shale in the Western Chongqing Block: high pressure, high organic content, and high brittle mineral content. Specifically, the Variational Mode Decomposition (VMD) is utilized to isolate high-frequency logging fluctuations induced by the strong heterogeneity of high brittle mineral content. The Convolutional Neural Network (CNN) acts as a spatial feature extractor to capture the intricate spatial distribution patterns associated with high organic content. Subsequently, the Bidirectional Long Short-Term Memory (BiLSTM) network models the long-range sequential depth dependencies, reflecting the continuous geomechanical evolution under high-pressure compaction gradients. Finally, the Attention (AT) mechanism dynamically prioritizes the most sensitive logging responses to rock mechanical properties under these complex geological constraints. The proposed VMD-CNN-BiLSTM-AT model was then used to estimate Young’s modulus and Poisson’s ratio in the Western Chongqing Block. The testing phase yielded strong predictive performance, achieving R² scores of 0.966 and 0.96 for Young’s modulus and Poisson’s ratio, respectively, which were approximately 10% higher than those of other conventional models. Therefore, the proposed model supports data-driven fracturing optimization in deep shale plays by precisely predicting mechanical properties. Full article

Aiming at the problem of heavy metal release from ultra-low-grade waste rock caused by the coupling of natural weathering and acid-rain leaching, black shale gravel of the Cambrian Series in western Hunan was taken as the research object. Gradient mechanical crushing was used to simulate physical weathering, and sulfuric–nitric acid-type simulated acid rain was prepared for continuous leaching experiments. Combined with ICP-MS monitoring and SEM-EDS characterization, the effects of crushing intensity on the physicochemical properties of leaching system and heavy metal release kinetics were systematically analyzed. The results showed that the pH of the leaching system presented three evolutionary stages: acid-dominated, alkaline transition and buffer stabilization. Heavy metal release could be divided into three types according to their occurrence forms: the sulfide-phase-sensitive type (Cd, Zn), secondary stable type (Pb), and silicate lattice bound type (Cu, Ni, Cr). The promotion effect of crushing on interface reaction activity showed diminishing marginal effect, and the particle fractal dimension increased from 2.15 to 2.67. It was concluded that the core controlling factor of heavy metal release risk is the selective exposure degree of occurrence mineral phases by physical disturbance. A coupling framework of “physical weathering–mineral exposure–release response” was established, providing a scientific basis for the differentiated management and control of heavy metals in filling sites. 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

Deep rock engineering faces the combined challenges of high in situ stress and dynamic disturbances. However, traditional constitutive models treat confining pressure and rate effects independently, leading to significant prediction errors under high confinement, and the underlying coupled mechanisms remain insufficiently understood. To address this, dynamic tests were conducted using an active confining pressure SHPB system under hydrostatic pressures of 0–30 MPa and loading rates of 2000–12,000 GPa·s⁻¹, with simultaneous acoustic emission and dissipated energy monitoring. A confining pressure-sensitive rate-dependent dual-scalar damage constitutive model was established, innovatively incorporating a Constraint Intensification Factor (CIF) and a viscous regularization technique to intrinsically couple confinement and rate effects. The results reveal a synergistic strengthening effect between confining pressure and loading rate, with higher confining pressure enhancing rate sensitivity. The proposed model accurately captures the elastic, peak, and post-peak segments of stress–strain curves, with peak stress errors below 5%, effectively overcoming the prediction deficiencies of traditional models under high confining pressures. These findings provide critical parameters and a reliable theoretical basis for deep rock engineering design. Full article

A review on the structure, properties, sustainability, and life cycle analysis of basalt fiber composites, emerging as a major sustainable alternative to traditional synthetic reinforcements such as glass and carbon fibers. Basalt fibers (BFs) are high-performance mineral fibers derived from volcanic rock with a high silica content. These fibers exhibit superior mechanical strength, excellent chemical resistance, and exceptional thermal stability across a broad temperature range. This review explores the multi-sectoral applications of basalt fibers, particularly within the energy and chemical industries. Specific focus is placed on their role as reinforcing agents in concrete and polymer matrix composites, where they provide enhanced durability and corrosion resistance. Central to this discussion is the environmental profile of basalt fibers. We evaluate recent life cycle assessments (LCAs) that compare the environmental gains of BF-reinforced structures. The analysis extends beyond environmental metrics to include the economic and social pillars of sustainability, highlighting basalt’s cost-effectiveness in corrosive environments and its safety as a non-carcinogenic material. This review concludes that basalt fibers offer a significant “green” advantage, encouraging wider industrial adoption. Full article