Search Results (1,723)

Excavation and dewatering are the primary factors governing diaphragm wall deformation and ground surface settlement in deep foundation pits. However, their coupled effects in soft-over-hard composite strata remain insufficiently understood. This study investigates a deep metro foundation pit in Jinan, China, and develops a three-dimensional hydro-mechanical coupled model in ABAQUS to simulate the complete staged excavation and dewatering process. The evolution of diaphragm wall lateral displacement, ground surface settlement, and pore-water pressure was systematically analyzed, and the simulation results were validated against field monitoring data. The results show that both excavation and dewatering induced significant wall deformation and surface settlement, with excavation playing the dominant role. The incremental lateral displacement of the diaphragm wall caused by excavation was approximately 2.6–3.8 times that caused by dewatering, while the corresponding ground surface settlement was 7.9–10.7 times greater. Owing to the strong restraint provided by the underlying rock stratum, the maximum lateral displacement of the diaphragm wall occurred at approximately 0.67 H_e, where H_e is the final excavation depth. The primary influence zone of ground surface settlement extended to approximately 2 H_e. In addition, dewatering altered the seepage field inside and outside the pit, leading to a continuous decrease in pore-water pressure within the pit, whereas the external pore-water pressure remained largely unchanged because of the seepage-barrier effect of the diaphragm wall. These findings provide practical guidance for the design and construction of deep foundation pits under similar geological conditions. Full article

Deep mining of geologically challenging deposits, such as narrow, steeply dipping orebodies, is increasingly pursued to meet the rising demand for mineral resources. However, the geotechnical stability of operations in such environments remains a persistent challenge. A paramount concern is the insufficiently understood mechanisms governing the surface subsidence and stability of underground excavations, which diverge significantly from those in flat or gently dipping deposits. This study bridges this gap through an integrated methodology applied to a deep cut-and-fill gold mine in China. We combined nine years (2016–2025) of SBAS-InSAR monitoring, utilizing 120 Sentinel-1 images corrected with precise orbit and atmospheric correction data, with a comprehensive three-dimensional (3D) numerical simulation. The results reveal a unique subsidence pattern: surface subsidence is highly localized, forming an elliptical basin directly above the orebodies, with a footwall movement angle exceeding 90°. Furthermore, the subsidence magnitude showed minimal progression despite increasing mining depth, with a maximum cumulative subsidence of only 9.3 mm. Numerical simulation confirmed these findings and demonstrated that underground shafts and tunnels remained stable under the sequential extraction of multiple orebody levels. This exceptional geotechnical response is attributed to a synergistic mechanism involving the intrinsic geomechanical advantages of the steeply dipping geometry, the low-disturbance nature of narrow-vein mining, and the crucial structural support provided by the backfilling. This study demonstrates the efficacy of cut-and-fill mining for ensuring operational safety and minimizing surface environmental impact in the deep mining of narrow, steeply dipping orebodies, providing critical insights for the sustainable exploitation of deep mineral resources. Full article

Accurate estimation of river discharge is fundamental for water resources management, flood forecasting, and drought monitoring in the Amazon River Basin. Rating curves, which relate water level (stage) to discharge, are the primary tool for streamflow estimation. This study evaluates traditional curve-fitting methods and machine learning algorithms for modeling rating curves at the two largest gauging stations in the Amazon River: Itacoatiara and Óbidos. The analysis is based on 70 stage–discharge measurements at Itacoatiara (2008–2023) and 176 measurements at Óbidos (1968–2023). Five modeling approaches were compared: Power Law, Linear Regression, Decision Tree, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed against official baseline rating curves maintained by Brazil’s National Water Agency (ANA) and the Geological Survey of Brazil (SGB/CPRM) using Root Mean Square Error (RMSE), coefficient of determination (r²), Mean Bias Error (MBE), Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE). Results indicate that ensemble-based machine learning methods, particularly XGBoost (RMSE = 7463 m³/s, NSE = 0.973 at Itacoatiara; RMSE = 18,378 m³/s, NSE = 0.872 at Óbidos), outperformed traditional methods. However, the Decision Tree exhibited overfitting that could not be resolved through pruning, depth limitation, or other strategies given the sample size. Traditional methods such as the optimized Power Law remain practical and transparent alternatives for operational use. The findings suggest that machine learning can complement traditional approaches for improving rating curve accuracy in large tropical rivers, with K-fold cross-validation used to assess variability and performance. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article

Groundwater is a key strategic resource underpinning water security, and its effective management requires reliable, high-frequency monitoring data. In mountainous regions such as the flysch Carpathians in southern Poland, natural springs are particularly sensitive indicators of aquifer system dynamics. This study analyzes the role of springs in the national groundwater observation and research network and identifies barriers to the implementation of automated monitoring of spring discharge. The research covered 28 springs operating within the regional monitoring network of the Polish Geological Institute—National Research Institute in the Carpathian region. Classical hydrogeological spring classifications were applied and complemented with proprietary criteria addressing formal-legal, technical, and environmental conditions affecting the feasibility of automation. The results show that all of the analysed springs exhibited a Meinzer’s variability index (V) exceeding 100%, and numerous objects showed a coefficient of variation (CV) above 150%, providing quantitative evidence that standard weekly manual measurements statistically fail to capture rapid flow dynamics and peak discharge events. To bridge the gap between hydrodynamic observations and monitoring logistics, this study introduces a novel methodological contribution: the F-T-S-N screening framework. This proprietary, multi-criteria classification quantifies Formal-legal, Technical, Structural, and Nature-environmental barriers to telemetry implementation. The application of this framework demonstrates that the main obstacles to modernization are non-technological. The proposed classification serves as a practical, transferable tool that supports the rational planning of monitoring network automation in other mountainous regions with similar hydrogeological conditions. Full article

To address the difficulty of determining the safe reserved thickness of the mining wall in the test block of the panel pillar at Tongkeng Mine, The stress of mining wall is comprehensively analyzed. Combined with numerical simulation method and field monitoring, the optimal wall thickness is determined. By differentiating each stress component, the mathematical equations governing the locations where extreme values of the stress components occur are derived, and the mathematical expressions for the extreme value positions of each stress component are further determined accordingly. Considering the geological characteristics and mining conditions of the experimental stope with panel pillars, the eastern mining wall of the test block is selected as the research object. A mining wall thickness range of 3 m to 8 m is designed, and the optimal safe reserved thickness of the mining wall is determined through numerical simulation. Based on the optimal mining wall retention thickness, stopping operations are carried out on the orebody of the experimental stope. Meanwhile, monitoring points are reasonably arranged from the upper-middle section to the middle of the mining wall, and real-time monitoring is performed on the stress variation data at each monitoring point during the entire stopping process of the test block. Theoretical analysis results show that the exact locations of the extreme values of each stress component can be accurately determined within the two-dimensional plane of the mining wall, among which the extreme value of the horizontal stress component appears at the midpoint of the mining wall thickness. Numerical simulation results indicate that both the stress and displacement of the mining wall exhibit a gradual decreasing trend with an increase in mining wall thickness. However, when the mining wall thickness exceeds 5 m, the reduction rate of stress and displacement slows down significantly, and the mining wall tends to become stable. Maintaining a mining wall thickness of 5 m in the experimental stope can generally ensure the safe recovery of the orebody. However, pronounced stress concentrations occur at the geometric corners of the mining wall, which result from stress retention caused by changes in the mining wall geometry. Meanwhile, the stress concentration in the mining wall is synchronized with that in the drilling galleries of the experimental stope, and varying degrees of failure occur in the drilling galleries at locations where stress concentration appears in the mining wall. Monitoring results show that the maximum stress borne by the drilling gallery is approximately 26 MPa, beyond which rock mass collapse and fragmentation are prone to occur. Full article

To address the technical challenge of large-area roof hanging and induced strong strata behaviors in deep mines with hard roof strata, a study on pressure relief using hydraulic fracturing technology was conducted, taking the 1012006 working face in the Yuanzigou Coal Mine as the engineering background. Through geological survey and key stratum theory analysis, a low-position key stratum located 23 m above the roadway roof was identified as the target layer for fracturing. True triaxial hydraulic fracturing experiments coupled with acoustic emission (AE) monitoring revealed a synchronous response characterized by a sudden drop in injection pressure and a rapid increase in AE counts. This established a quantitative correlation between rock mass fracturing and AE characteristics, providing a theoretical basis for field microseismic monitoring. Based on the “dual-borehole synergy” borehole layout principle, a fracturing network comprising 6 drilling fields and 12 directional long boreholes was designed, with a total drilling length of 5727 m and 120 planned fracturing stages. Specialized equipment was selected for implementation. Field monitoring results demonstrated: a maximum fracturing influence radius of 27.8 m; that the average daily frequency and total energy of microseismic events decreased by 50.65% and 27.73%, respectively; and that the stress in the deep part of the roadway decreased by 17.69%. These results confirm the effective improvement of the roof stress environment and the successful achievement of the expected pressure relief and rockburst prevention effect. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article

At around 8:40 a.m. on 17 July 2024, the Wanshuitian landslide in the Three Gorges Reservoir Area (TGRA) experienced a deformation failure characterized by thrust load-caused deformations and high-speed sliding. Using geological surveys and unmanned aerial vehicle (UAV) photography, this study divided the Wanshuitian landslide area into five zones: sliding initiation (A1), secondary disintegration (A2), main accumulation (B1), right falling (B2), and left falling (B3) zones. Through monitoring data analysis and GeoStudio-based numerical simulations, this study revealed the mechanisms behind the landslide failure mode characterized by slope sliding approximately along the strike of the rock formation under the coupling effect of hydrological and geological conditions. The results indicate that factors inducing the landslide failure include the geomorphic feature of alternating grooves and ridges, the lithologic assemblage characterized by interbeds of soft and hard rocks, the slope structure with well-developed joints, and the sustained heavy rains in the preceding period. In the Wanshuitian landslide area, mudstone valleys are prone to accumulate rainwater, which can infiltrate directly into the weak interlayers of rock masses and soften the rock masses. Multi-peak rain events with a short time interval serve as a critical factor in groundwater recharge. Within 17 days preceding its failure, the Wanshuitian landslide experienced a superimposed process of heavy and secondary rain events with a short interval (four days). Rainwater from the first heavy rain event failed to completely discharge during the short interval, while the secondary rain event also caused rainwater accumulation. These led to a continuous rise in the groundwater table, a constant decrease in the shear strength of the slope, and ultimately the landslide instability. Since the landslide sliding in the dip direction of the rock formation was impeded, the main sliding direction of the landslide formed an angle of 88° with this direction. This led to a unique failure mode characterized by slope sliding approximately along the strike of the rock formation. Based on these findings, this study proposed characteristics for the early identification of the failure of similar landslides, aiming to provide a robust scientific basis for the monitoring, early warning, and prevention and control of the failure of similar landslides. Full article

Gas concentration series in coal mining faces are jointly affected by multiple coupled factors, including geological conditions, mining disturbances, ventilation organization, and gas drainage intensity, and therefore exhibit pronounced nonstationarity, strong fluctuations, spatiotemporal correlations across multiple monitoring points, and occasional abrupt spikes. To address these challenges, this study proposes a gas concentration prediction and early-warning method that integrates CEEMDAN–SST with GraphPINN-TimesFM (Graph Physics-Informed Neural Network–Time Series Foundation Model). First, based on multi-source monitoring data such as wind speed, gas concentrations at multiple monitoring points, and equipment operating status, anomaly removal, operating-condition segmentation, and change-point detection are performed to construct stable operating-state labels. Feature selection is then conducted by combining optimal time-lag correlation, Shapley value contribution, and dynamic time warping. Second, WGAN-GP is employed to augment samples from minority operating conditions, while CEEMDAN–SST is used to decompose and reconstruct the target series so as to reduce the interference of nonstationary noise and enhance sequence predictability. On this basis, TimesFM is adopted as the backbone for long-sequence forecasting to capture long-term dependency features in gas concentration evolution. Furthermore, GraphPINN is introduced to embed the topological associations among monitoring points, airflow transmission delays, and convection–diffusion mechanisms into the training process, thereby enabling collaborative modeling that integrates data-driven learning with physical constraints. Finally, the predictive performance, early-warning capability, and interpretability of the proposed model are systematically evaluated through regression forecasting, warning discrimination, and Shapley-based interpretability analysis. The results demonstrate that the proposed method can effectively improve the accuracy, robustness, and physical consistency of gas concentration prediction under complex operating conditions, thereby providing a new technical pathway for gas over-limit early warning and safety regulation in coal mining faces. Full article

Acoustic emission (AE) and digital image correlation (DIC) techniques enable real-time capture of damage signals and full-field deformation at anchored rock–concrete interfaces under shear loading, which is critical for quantitatively characterizing freeze–thaw (F-T) degradation and preventing geological disasters in cold regions. This study synchronously monitored full-shear-process AE signals using a broadband AE system (150 kHz resonant frequency, 5 MS/s sampling) and captured high-precision full-field deformation via a 5-megapixel monocular DIC system (25 fps). F-T cycle and direct shear tests were conducted on sandstone–concrete anchored specimens with varying F-T cycles and anchor depths to investigate their effects on shear mechanical properties, AE characteristics and failure modes. Results show that AE peak ring count first decreases by 44.9% then increases by 56.5%, while cumulative ring count exhibits a three-stage evolution. Shear crack proportion first decreases then increases, with tensile failure remaining dominant throughout. DIC reveals that F-T cycles shift failure from crack propagation to surface delamination and interface slip, while different anchor depths induce distinct failure patterns. This study confirms that AE and DIC can accurately characterize F-T degradation, providing a reliable non-destructive monitoring method for cold-region anchorage engineering. Full article

The construction sector is experiencing increasing demand for deep underground structures in urban environments, where excavations frequently intersect fractured aquifers. Such conditions pose significant risks to structural stability and long-term durability due to groundwater inflow and elevated hydrostatic pressures. This study investigates the influence of deep underground construction on fractured aquifer systems using the Abu Dhabi Plaza development in Kazakhstan as a case study. An integrated methodological approach combining hydrogeological monitoring, hydrochemical analysis, and engineering–geological testing was applied. Groundwater levels were monitored using observation wells, while triaxial and uniaxial compression tests were conducted to evaluate the mechanical properties of rock and soil materials. Hydraulic gradients, flow velocities, and hydrostatic pressures were estimated using Darcy’s law and the Boussinesq equation, supported by GIS-based spatial analysis. Groundwater mineralisation is consistently represented in this study by total dissolved solids (TDS), expressed in g/L. The results indicate that groundwater in the Quaternary aquifer is fresh to slightly mineralised, with TDS ranging from 0.47 to 1.50 g/L, whereas groundwater in the fractured Ordovician aquifer exhibits a more stable hydrochemical regime with TDS values of 0.72–0.73 g/L. Statistical analysis identifies two primary controls on groundwater chemistry: (i) natural geochemical processes associated with water–rock interaction and (ii) technogenic influences related to urban activities. Hydrodynamic calculations indicate a hydraulic gradient of approximately 0.136, a filtration velocity of about 0.35 m/day, well discharge reaching 0.11 L/s, and hydrostatic pressure ranging from 1.45 to 2.81 atm. Groundwater drawdown caused by excavation dewatering reached 29–30 m. The findings demonstrate that groundwater inflow is primarily controlled by fracture-controlled permeability and structural heterogeneity within the aquifer system. These results highlight the importance of integrated hydrogeological and hydrochemical assessment, in which TDS serves as the principal quantitative indicator of groundwater mineralisation, for the effective management of groundwater-related risks during deep underground construction. Full article

With the rapid expansion of metro tunnel construction, safety risks such as collapse, water inrush, and gas explosion have become increasingly critical. Existing warning models often lack fine-grained disaster type identification and dynamic risk assessment capabilities. This paper proposes a two-stage intelligent warning framework based on multi-source data fusion. First, a dual-autoencoder structure (MLP-AE and LSTM-AE) extracts deep features from static geological parameters and dynamic monitoring sequences. Then, a multilayer perceptron (MLP) classifier identifies four typical states: normal, collapse, water/mud inrush, and gas explosion. Subsequently, a regression model predicts a continuous risk score, mapped to three risk levels: Safe, Moderate Risk, and Significant Risk. Experimental results demonstrate that, compared with Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Bayesian Network (BN), the proposed framework achieves superior performance in risk level identification, with an accuracy of 91% and an F1-score of 0.87. Notably, it exhibits particularly strong recall for severe (Level III) risks, which is crucial for practical engineering applications. The proposed framework provides a practical and intelligent approach for safety warning in metro tunnel construction. Full article

Karst aquifers present unique challenges for groundwater level prediction due to their dual-porosity structures and highly nonlinear hydrological responses. This study systematically evaluates nine machine learning and deep learning models (RF, XGBoost, LSTM, CNN, Transformer, N-BEATS, CNN-LSTM, Seq2Seq-LSTM, and Attention-Seq2Seq-LSTM) for rainfall-driven groundwater level forecasting in the Maocun subterranean river catchment, Guilin, Guangxi, China. Two years of hourly high-frequency data from three monitoring sites representing distinct hydrogeological zones (recharge, flow, and discharge) were employed within a multidimensional evaluation framework integrating single-step accuracy, multi-step stability, and computational efficiency. Results indicate that the Transformer achieved the highest single-step prediction accuracy, attaining the lowest RMSE (0.130–0.606 m) and highest R² (0.813–0.965) across all three sites. CNN-LSTM offered the best balance between predictive performance and computational cost, requiring an average training time of only 27.97 s and 28.0 convergence epochs. N-BEATS demonstrated superior long-term stability in 12-steps-ahead forecasting, achieving R² = 0.914 at ZK1, outperforming all other architectures. More fundamentally, hydrogeological complexity exerted a dominant control on predictive skill that systematically outweighed differences arising from model architecture. All models yielded R² below 0.813 at the geologically complex ZK2 site, whereas R² exceeded 0.950 across all models at ZK1, indicating that aquifer complexity, rather than algorithm selection, constitutes the primary constraint on prediction feasibility. This study presents the first application of N-BEATS to karst groundwater level forecasting and proposes a replicable multidimensional evaluation framework, providing a scientific reference for intelligent modelling of complex karst systems. Full article

Defining the near-surface signal reflecting the deep sub-surface leakage is a critical challenge in the risk assessment of geologic carbon storage (GCS) projects, often exacerbated by decoupled deep-to-shallow modeling. This study quantifies the mass distribution and phase evolution of leaked CO₂ through deep reservoir-caprocks, intermediate aquifer, and near-surface soil, thereby showing the sub-surface retention characteristics and the detectability of near-surface signals. A geological model from the deep reservoir to the soil layer was constructed to simulate CO₂ leakage through the caprock and migration into overlying strata in 1000 years. Using the simulator of GPSFLOW, this study evaluates the evolution of fluid phases and the mass distribution during the injection for 100 years and the post-injection periods. The results indicate that (1) at the moment the injection ceases, 87.43–99.06% of the CO₂ remaining within the system is retained within the reservoirs, with less than 8.42% reaching the intermediate aquifer. Remarkably, although the CO₂ ultimately reaching the near-surface soil is less than 0.00073% of the total mass retained within the system, this mass accumulation translates to a concentration anomaly with a signal-to-noise ratio of 368 relative to the background baseline. (2) Sensitivity analysis reveals that the injection rate affects the timing of fluid transport—a tenfold increase in injection rate (from 3.17 to 31.7 kg/s) accelerates the upward movement of CO₂, advancing its arrival at the near-surface by 15 years without changing the overall mass partitioning. The permeability anisotropy ratio affects CO₂ migration and phase distribution—decreasing the vertical to horizontal permeability ratio (1, 0.5, 0.25, 0.125) reduces connectivity, which delays the upward transfer and increases the amount of the aqueous CO₂. However, specifically in the soil layer, the aqueous CO₂ accumulation reveals a non-monotonic trend that peaks at an intermediate ratio of 0.25. (3) CO₂ shows a cascading distribution across formations where reservoirs provide the primary storage, and the intermediate aquifer reduces the mass available for near-surface accumulation. This attenuation effect significantly reduces the CO₂ mass that reaches the soil layer, thereby controlling the strength and duration of near-surface environmental signals. This work offers a theoretical reference for formulating near-surface monitoring strategies for CO₂ leakage in GCS. Full article