Search Results (239)

To address the limitations of traditional energy dissipation technologies, such as difficulty in arranging energy dissipators due to narrow river valleys and complex geological conditions and the low energy dissipation efficiency of existing air jet collision methods, this study proposes a novel structural form of a horizontal opposing jet energy dissipator. Water is diverted to the open area downstream of the reservoir hub via diversion pipelines, and energy dissipation is achieved through horizontal opposing collision of jets in the air. Focusing on this new energy dissipator, numerical simulations combined with physical experiments were conducted to investigate its energy dissipation characteristics, with the dimensionless parameters l/d (collision distance/pipeline diameter) and Reynolds number (Re) as the main variables. The results indicate that two opposing jets formed a crown-shaped water jet after horizontal collision in the air. The rising height in the Z-direction and expanding width in the Y-direction of the crown-shaped water jet exhibit a negative correlation with l/d and a positive correlation with Re. Energy dissipation was achieved through jet collision, mixing, friction, diffusion, aeration, and fragmentation in the air. This energy dissipation method improved the energy dissipation rate by extending the collision time and mixing length of jets in the air. The primary factors influencing the energy dissipation rate were l/d and Re. Under the study conditions, the energy dissipation rate of jet collision in the air ranged from 16.25% to 39.54%. The energy dissipation efficiency exhibits a negative correlation with l/d and a positive correlation with Re. This study provides a new approach for energy dissipation in hydraulic engineering. Full article

In urban underground space construction using shield tunnelling, the geological conditions ahead of the tunnel face are often uncertain. Without timely and accurate classification of the ground type, mismatches in operational parameters, uncontrolled costs, and schedule risks are likely to occur. Using observations from an earth pressure balance (EPB) project on an urban railway, a data-driven classification framework is developed that integrates shield tunnelling operating measurements with physically derived quantities to discriminate among soft soil, hard rock, and mixed strata. Principal component analysis (PCA) is performed on the training set, followed by a systematic comparison of tree-based classifiers and hyperparameter optimization strategies to explore the attainable performance. Under unified evaluation criteria, a categorical bosting (CatBoost) model optimized by a Nevergrad combination strategy (NGOpt) attains the highest test accuracy of 0.9625, with macro-averaged precision and macro-averaged recall of 0.9715 and 0.9716, respectively. To mitigate optimism from single-point estimates, stratified bootstrap intervals are reported for the test set. A Monte Carlo experiment applies independent perturbations to the PCA-transformed features, producing low label-flip rates across the three classes, with only minor changes in probability calibration metrics, which suggests consistent decisions under sensor noise and sampling bias. Overall, within the scope of the considered EPB project, the study delivers a compact workflow that demonstrates the feasibility of uncertainty-aware ground-type classification and provides a methodological reference for developing decision-support tools in underground tunnel construction. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article

The study evaluates the long-term environmental performance of natural filtration dams for leachate treatment at a municipal solid waste landfill. Field measurements of a system operating for 24 years, equipped with natural clay-loam filtration barriers, provide empirical validation for assessing the effectiveness and durability of natural material-based treatment approaches. Hydrogeological studies, including well drilling, water sampling, and comprehensive chemical analysis, demonstrate that the cascade filtration system achieves pollutant removal efficiencies of 80–95% for major contaminants. Physical property measurements reveal progressive density reduction from 1005 to 994 kg/m³ and viscosity decreases from 1.048 to 1.011 cSt across the treatment system. Numerical simulations demonstrate that contaminant transport under actual site conditions remains diffusion-dominated over multi-decadal timescales, with aquifer concentrations remaining below 1% of source values after 50 years. Parametric studies reveal that density-driven convective fingering develops only at source concentrations exceeding 100 g/L. The findings validate the long-term viability of natural geological barriers combined with cascade filtration systems for cost-effective leachate treatment, demonstrating that preliminary treatment through natural filtration effectively suppresses gravitational instabilities and protects underlying aquifers. Full article

The mining industry places high priority on occupational safety, process quality and operational efficiency. Roadheaders are widely deployed in coal mines to support fully automated excavation, reducing workers’ physical strain and improving overall safety. This article examines an automatic control system for a roadheader cutting head designed to increase mining efficiency, reduce energy consumption and maintain stable performance under varying coal and rock conditions. The system integrates advanced control algorithms with geological strength index (GSI) analysis and asynchronous motor control strategies. GSI-based adaptive speed control conserves energy and increases cutting efficiency compared to manual control. By reducing dynamic load fluctuations, transitions between different cutting zones become smoother, which decreases equipment wear. The proposed control system incorporates speed feedback loops that use a proportional–integral (PI) controller with field-oriented control (FOC), as well as super-twisted sliding mode control (STSMC) with FOC. FOC with STSMC improves roadheader productivity by applying advanced control strategies, adaptive speed regulation and precise geological strength analysis. It is also better able to handle disturbances and sudden loads thanks to STSMC’s nonlinear control robustness. The result is safer, more efficient, and more cost-effective mining that can be implemented across a wide range of underground mining scenarios. Full article

Compressed air energy storage in aquifers (CAESA) is a promising technology for large-scale, long-duration energy storage. The initial bubble, also known as cushion gas, is a prerequisite for system operation, as it creates the storage space and provides pressure support. However, the optimal amount of cushion gas needed to satisfy both energy storage scale and system cycle time (SCT) remains insufficiently studied. In this work, we investigate the relationship between cushion-gas masses and SCT under various energy storage scales using numerical simulations, and further analyze its impact on the maximum achievable energy storage scale through an orthogonal design encompassing nine geological conditions. Simulation results indicate that aquifer permeability, depth, and thickness impose a physical upper limit on achievable storage scales. Below this threshold, increasing cushion-gas mass approximately linearly enhances SCT, while beyond it, performance gains saturate. The effect of the air bubble on system performance is also influenced by well screen length. Sensitivity analysis suggests that larger injection masses are beneficial under high-permeability and deeper burial conditions, whereas excessive injection under unfavorable geological conditions can lead to inefficiency and wasted resources. Based on these findings, the recommended injection gas masses for different energy storage scales under the ideal model are provided, along with suggestions for gas injection configurations based on various geological conditions. This work provides a new approach for the design of initial bubble injection for a CAESA system. Full article

Underground gas storage (UGS), encompassing hydrogen, natural gas, and compressed air, is a cornerstone of large-scale energy transition strategies, offering seasonal balancing, security of supply, and integration with renewable energy systems. However, the complexity of geological conditions, multiphysics coupling, and operational uncertainties pose significant challenges for UGS design, monitoring, and optimization. Artificial intelligence (AI)—particularly machine learning and deep learning—has emerged as a powerful tool to overcome these challenges. This review systematically examines AI applications in underground storage types such as salt caverns, depleted hydrocarbon reservoirs, abandoned mines, and lined rock caverns using bibliometric and knowledge-graph analysis of 176 publications retrieved from the Web of Science Core Collection. The study revealed a rapid surge in AI-related research on UGS since 2017, with underground hydrogen storage emerging as the most dynamic and rapidly expanding research frontier. The results reveal six dominant research frontiers: (i) AI-assisted geological characterization and property prediction; (ii) physics-informed proxy modeling and multi-physics simulation; (iii) gas–rock–fluid interaction, wettability, and interfacial behavior prediction; (iv) injection-production process optimization; (v) intelligent design and construction of underground storage, especially salt caverns; and (vi) intelligent monitoring, optimization, and risk management. Despite these advances, challenges persist in data scarcity, physical consistency, and generalization. Future efforts should focus on hybrid physics-informed AI, digital twin-enabled operation, and multi-gas comparative frameworks to achieve safe, efficient, and intelligent underground storage systems aligned with global carbon neutrality. Full article

Caprock sealing capacity is paramount for the safety and efficacy of geological carbon storage. This study investigates the evolution of mudstone caprock sealing capacity under CO₂–mechanical coupling, integrating experimental rock mechanics with fluid–solid coupling numerical simulations. Laboratory experiments reveal that caprock permeability exhibits strong stress sensitivity, decreasing exponentially with increasing effective stress. The stress sensitivity coefficient is highly dependent on initial pore pressure and porosity, being greatest under low-pore-pressure and high-porosity conditions. Furthermore, permeability loss during loading is partially irreversible due to plastic deformation. Numerical simulations, conducted using an integrated Petrel + Visage + Eclipse workflow, quantify the influence of caprock physical and mechanical properties on sealing capacity during CO₂ injection. The results demonstrate that vertical total stress increases with increasing porosity, Young’s modulus, and permeability, with permeability exerting the most significant control. Conversely, vertical effective stress decreases with increases in these parameters, with porosity causing the largest variation. We conclude that lower caprock permeability and porosity are most critical for enhancing sealing integrity, while a higher Young’s modulus improves mechanical stability. These findings provide a theoretical basis and practical methodology for evaluating caprock sealing capacity and ensuring the secure storage of CO₂. Full article

Coal mining faces critical challenges due to variable geological conditions that affect intelligent mining and safe production. Ground-penetrating radar (GPR), a high-resolution and non-destructive sensing technology, is essential for precise geological detection. However, underground electromagnetic interference, multiple reflections, and complex media significantly degrade the signal-to-noise ratio (SNR), causing reflection signals to be obscured and geological interfaces to become blurred, thereby hindering accurate subsurface interpretation. Traditional denoising methods struggle to extract weak reflection signals under such complex noise conditions. To address these challenges, this study proposes a physics-guided conditional diffusion model that integrates physical constraints with deep learning to achieve intelligent denoising and weak-signal recovery for high-noise GPR data. Specifically, a dual-path GMM probabilistically models both feature signals and complex noise, while incorporating the wave equation ensures physical consistency with electromagnetic propagation. Experiments using a hybrid dataset combining field-measured noisy data and simulated features—evaluated using SSIM, PSNR, MAE, peak alignment, and structural continuity—demonstrate that the proposed method outperforms existing techniques in both noise suppression and signal reconstruction. Field tests in underground coal mines further confirm its practical applicability. Full article

This study addresses the stability and deformation control of the Xiling auxiliary shaft in the Sanshandao Gold Mine during excavation, under the complex geological conditions of high in situ stress, high pore pressure, and elevated geothermal gradients. A thermal–hydraulic–mechanical (THM) coupling numerical model is developed to investigate the stress distribution, deformation mechanisms, and long-term stability of the surrounding rock under multi-physical interactions. Meanwhile, the influence of excavation rate on rock stability is analyzed. The results indicate that excavation induces significant stress redistribution, with stress concentrations in high-elastic-modulus strata, where the maximum compressive and tensile stresses reach 15.9 MPa and 14.1 MPa, respectively. The maximum displacement occurs in low-stiffness rock layers (around 1400 m depth), with a total magnitude of 1139 mm, primarily resulting from unloading relaxation, pore pressure reduction, and thermal contraction. Excavation rate strongly affects the temporal evolution of deformation: faster excavation leads to greater instantaneous displacements, whereas slower excavation suppresses displacement due to the sustained influence of thermal contraction. Based on these findings, particular attention should be paid to the low-stiffness strata near 1400 m depth during the construction of the Xiling auxiliary shaft. A combined support system consisting of high-prestress rock bolts, lining, and grouting is recommended for deformation-concentrated zones, while excavation rates should be optimized to balance efficiency and safety. Furthermore, long-term monitoring of temperature, pore pressure, and displacement is essential to achieve dynamic risk control. These results provide valuable theoretical and engineering insights for the safe construction and stability management of deep mine shafts. Full article

The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise interference. This paper proposes a microseismic P-wave arrival time automatic picking model that integrates physical constraints with a deep learning architecture. This study trained and optimized the model using a high-quality, manually labeled dataset. A systematic comparison with the AR picker algorithm and the short-term–long-term average ratio method revealed that this model achieved a precision of 96.60%, a recall of 90.59%, and an F1 score of 93.50% on the test set, with a P-wave arrival time-picking error of less than 20 ms. The average arrival time error was only 5.49 ms, significantly outperforming traditional methods. In cross-mining area generalization tests, the model performed excellently in two mining areas with consistent sampling frequencies (1000 Hz) and high signal-to-noise ratios, demonstrating good engineering transferability. However, its performance decreased in a mining area with a higher sampling rate and stronger noise, indicating its sensitivity to data acquisition parameters. This study developed a high-precision, robust, and potentially cross-domain adaptive model for automatically picking microseismic P-wave arrival times. This model provides support for the automation, precision, and intelligence of coal mine microseismic monitoring systems and has significant practical value in promoting real-time early warning and risk prevention for mine dynamic hazards. Full article

Based on the problem of water and sand inrush caused by the infiltration and failure of the clay layer at the bottom of the loose layer in shallow coal seam mining in eastern China, this study adopts the Particle Flow Code numerical simulation method to conduct multi-physics field coupling analysis. Based on the geological conditions of Taiping Coal Mine in Shandong Province, a two-dimensional water sand clay coupling model was constructed to systematically simulate the entire process of permeability failure of clay layers under different mining crack widths (5–20 mm). The permeability failure mechanism was revealed through porosity distribution, particle contact number, and contact force evolution laws. The numerical simulation results show that with the increase in crack width, the speed of contact reduction is faster, the speed of water and inrush is faster, and the time is shorter. The process of infiltration failure can be divided into two stages: the first stage is the clay infiltration deformation stage, and the second stage is the water inrush and sand collapse stage. In addition, the larger the width of the crack, the greater the contact force, and the shorter the time of infiltration failure and water and sand bursting experienced. The quantitative relationship between the width of mining induced cracks and permeability failure was revealed, and a critical discrimination index for permeability failure in clay layers was established, providing theoretical support for optimizing safe mining parameters and preventing water and sand inrush disasters in porous aquifers. Full article

The migration of the long-lived isotope technetium-99 (half-life 2.1 × 10⁵ years) presents a significant challenge for the deep geological disposal of radioactive waste. This study investigates the immobilization of technetium by carbon steel corrosion products under aerobic and anaerobic conditions simulating the Yeniseysky site (Krasnoyarsk Region, Russia), a proposed location for a Deep Geological Repository (DGR). Over time, the degradation of barrier materials is expected to allow low-salinity solutions to be brought into contact St3 steel, the intended container material for vitrified radioactive waste in the Russian context, leading to crevice corrosion. The findings demonstrate that carbon steel containers act not merely as a physical barrier but also as a chemical barrier by facilitating the reductive immobilization of technetium. The most effective reduction of technetium was observed in the presence of ferrihydrite as a corrosion product under both aerobic and anaerobic conditions, as indicated by distribution coefficient (K_d) values ranging from 1.4 × 10³ to 1.6 × 10³ cm³/g. However, the presence of bentonite clay can diminish the efficiency of this process by adsorbing corrosion products, resulting in a 50% reduction in the distribution coefficients. In contrast, leaching products from aluminophosphate glass and cement had a less pronounced effect on technetium immobilization, causing a decrease in distribution coefficients of no more than 30%. The results of this research can be applied to model the long-term behavior of technetium in the evolving environment of a geological radioactive waste repository. Full article

Shield tunneling beneath existing railways remains a critical challenge in urban infrastructure development, as it risks destabilizing overlying soil structures and compromising railway safety. This study presents an integrated methodology combining physical model tests and three-dimensional numerical simulation, validated by their mutual agreement, to capture the settlement and deformation induced by twin shield tunneling beneath an operational railway under the complex geological conditions of the Qingdao Metro. A parametric study was subsequently conducted to systematically evaluate the influence of critical construction parameters, including grouting pressure, grout stiffness, and chamber pressure, on railhead settlement. Additionally, a comparative analysis assessed the effectiveness of settlement control measures, including D-type beam reinforcement, deep-hole grouting reinforcement, and their combined application. Results show that railhead deformation primarily manifests as settlement, with cumulative effects from sequential tunneling of the left and right lines. Proximity to fault zones intensifies crown subsidence, while tunneling induces significant soil stress relaxation, particularly in geologically weaker strata. Within optimal ranges, increased grouting pressure, chamber pressure, and grout stiffness effectively reduce railhead settlement; however, their efficacy diminishes beyond specific thresholds. The combined D-type beam and deep-hole grouting reinforcement scheme proved most effective in controlling settlement, ensuring railway operational safety and construction stability. These findings provide essential theoretical and practical guidance for optimizing shield tunneling strategies in complex urban environments, enhancing the safety and reliability of critical railway infrastructure. Full article

The stability of pile foundation is influenced by many interacting factors, particularly geological conditions. Quantifying the impact of physical and mechanical soil properties on pile stability is critical for achieving optimal design outcomes. This study investigates the sensitivity of key soil parameters and validates the findings with a case study of a university building in Kashkar, Xinjiang, China. A three-dimensional pile–soil model was developed in Abaqus and calibrated with static load test data. Variable control and orthogonal experiments were conducted to examine settlement patterns and ultimate bearing capacity under varying soil parameters. Settlement and ultimate bearing capacity were adopted as stability indicators. Sensitivity analysis was performed through multi-factor variance analysis, sensitivity analysis of factors (SAF), and variance inflation factor (VIF) collinearity analysis. The results show that the most influential parameters are the friction coefficient of the soil above the pile tip, the Poisson’s ratio of the pile-end soil, the Poisson’s ratio of the soil above the pile tip, the friction coefficient of the pile-end soil, and the elastic modulus of the pile-end soil. These findings provide a quantitative basis for optimizing design parameters and improving the efficiency and reliability of pile foundation design in sandy soil regions. Full article