Search Results (1,266)

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

Existing studies on energy tunnels mainly focus on short-term heat transfer and neglect long-term thermal accumulation. This paper establishes a one-dimensional unsteady heat transfer model using Robin boundary conditions, considering air–lining coupled heat transfer and seasonal tunnel air temperature variations. The model is verified with experimental and numerical results, and the relative error is less than 1%. Simulations of 20-year continuous operation show that the host rock temperature presents obvious periodic fluctuations. The thermal influence zone expands rapidly at the initial operation stage and gradually stabilizes. Sensitivity analysis indicates that thermal conductivity, air flow velocity and circulating fluid velocity significantly affect the long-term thermal performance. Higher thermal conductivity speeds up heat diffusion, higher air velocity strengthens convective heat transfer, and higher fluid velocity improves heat exchange efficiency but increases pumping consumption. The model can accurately predict long-term temperature evolution, providing theoretical support for the design and operation optimization of energy tunnels. Full article

In recent years, China’s urban underground rail transit has developed rapidly, and the development of underground space has become increasingly complex, exposing the system to multiple operational risks such as structural instability, excessive deformation, equipment failures and emergencies. Existing studies often evaluate individual hazards or isolated stakeholder risks, while insufficient attention has been paid to how sudden events interact and propagate as disaster chains. To address this gap, this study develops a disaster-chain network framework for operational risk management in underground rail transit. Twenty sudden disaster risk events are first identified through literature review, expert consultation, system investigation, and HAZOP (Hazard and Operability) analysis. A database of 595 historical events is then used to construct co-occurrence and adjacency matrices. And the Jaccard index is used only to quantify association strength, while temporal order, HAZOP-based causal screening, and expert verification are introduced to distinguish plausible triggering relationships from simple correlations. Network indicators, including degree, betweenness, modified clustering coefficient, path length, connectivity, and edge vulnerability, are applied to identify critical nodes and propagation paths. The results indicate that functional failure of civil structures, fire, and crowd stampede are the dominant risk nodes. The proposed framework provides a transparent and replicable basis for prioritizing monitoring, emergency response, and link-cutting mitigation measures. The findings are intended as system-specific decision support rather than universal risk rankings and should be updated when new local operational data become available. Full article

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence. Full article

To achieve scientific cost forecasting, this study investigates structural changes in mining cost driven by the widespread adoption of mechanized mining, increased mining depths, and significant operational variations. Based on the backpropagation (BP) neural network, this study systematically analyzes the cost-composition characteristics of modern mining operations and applies activity-based costing to achieve refined cost accounting for each mining operation unit. Ten key influencing factors, including working space, stope temperature, stope depth, haulage distance, worker seniority and work efficiency, scraper efficiency, equipment service life, fuel and lubricant consumption rates, are identified by analyzing cost variation patterns. Principal component analysis (PCA) is used to reduce the dimensionality of the ten factors to simplify this model and enhance prediction accuracy. The PCA-BP neural network mining cost forecasting model is built with the principal components extracted as input variables. Actual cost data from an underground metal mine in Shandong Province is used for our model training and validation, with adopting linear regression, eXtreme Gradient Boosting (XGBoost), and a traditional BP neural network as the comparison models for performance evaluation. Our prediction results indicate that the PCA-BP model achieves an average relative error of 3.80% and a root mean square error of 1.43, both significantly outperforming the comparison models. The results demonstrate superior predictive accuracy and stability of our model. Validated with data from a typical deep mechanized gold mine in eastern China, the PCA-BP cost forecasting model requires parameter retraining based on local production conditions for applications in other regions. This study confirms that the model aligns well with the cost characteristics of modern underground metal mines and produces effective predictions, offering reliable quantitative support for the development of cost control strategies and optimization of cost planning in mining enterprises. Full article

Underground heritage represents a hidden urban resource for cultural regeneration and sustainable tourism, preserving historical layers, wartime memory, and local identity. Positioning the shelters as a form of Underground Built Heritage (UBH), this study examines how concealed civil-defense spaces can be reinterpreted as local cultural heritage resources before systematic reuse. However, enclosed and unfamiliar spaces are often perceived as risky, making adaptive reuse socially sensitive. This study investigates Jingdezhen’s underground air-raid shelters through a scenario-based survey and partial least squares structural equation modeling (PLS-SEM). Using an extended Value-Attitude-Behavior (VAB) framework incorporating perceived authenticity, anticipated affective identification, safety assurance, and perceived risk, this study identifies factors influencing pre-development public acceptance. Results show that public acceptance is shaped by cognitive evaluation of value and anticipated affective identification, while perceived risk constrains behavioral intentions. Perceived authenticity enhances value perception and anticipated affective identification; perceived value strengthens attitudes; safety assurance shows a small but statistically significant negative association with perceived risk, although most variance in perceived risk remains unexplained; and an exploratory moderation analysis further suggested that perceived risk may weaken the attitude–visit intention relationship. Although the estimated model showed a relatively high SRMR, the results are interpreted as prediction-oriented ex ante evidence rather than as a covariance-based model with strong global fit. These findings provide prediction-oriented ex ante evidence for the sustainable reuse of underground heritage, supporting heritage interpretation, risk management, and urban regeneration aligned with SDG 11. Full article

The synergistic utilization of post-mining spaces and geothermal energy through underground seasonal thermal energy storage (USTES) provides a promising pathway for sustainable heating and the low-carbon redevelopment of mining regions. To advance the thermal management and reveal the thermo-hydraulic evolution patterns within these repurposed environments, this study proposes an integrated approach that utilizes post-mining roadways as heat storage reservoirs, within the scope of a single idealized case study. A comprehensive USTES heating system model was established to systematically evaluate operational characteristics and environmental impacts under diverse conditions assuming homogeneous rock properties and idealized thermal boundaries. Results demonstrate that the surrounding ground temperature and the low thermal conductivity of the rock mass contribute to limiting heat dissipation and maintaining stable seasonal storage performance. For a roadway with a 20,000 m³ water storage capacity and an optimal 3900 m² solar collector area, the system successfully satisfies the thermal demand of 30,000 m² of building area. The configuration achieves 1239 MWh of cumulative heat storage over a 245-day cycle, maintaining a direct heating-to-heat-pump-upgraded heating ratio of 1.02. Furthermore, the implementation of variable-frequency thermal management strategies demonstrates remarkable economic and environmental superiority, yielding a 35.8% cost reduction compared to coal-fired heating, an overall energy saving rate of 77.5% relative to electric heating systems and a 13.5% decrease in CO₂ emissions relative to gas-fired systems. This research provides fundamental design parameters for the synergistic exploitation of mineral and geothermal resources, advancing the development of green heating and the sustainable utilization of post-mining spaces. Full article

As the development and utilization of underground spaces in coastal cities receive growing emphasis and continue to expand, the secondary disasters of underground flooding triggered by storm surges have become increasingly frequent in recent years. Consequently, the need for emergency evacuation in these spaces has grown more urgent, making the challenge of safe evacuation increasingly critical. However, the classical social force model shows notable limitations in simulating such scenarios, particularly in its lack of characterization of hydrodynamic resistance, heterogeneous pedestrian mobility, and organized guidance mechanisms. Therefore, this paper proposes an improved social force model for more realistically simulating the microscopic dynamics of pedestrians in underground floodwater environments. By extending the classical model, a flood resistance force term is introduced. Furthermore, the model comprehensively considers the varying speeds of pedestrians with heterogeneous attributes—such as age, height, and gender—under different water depths, quantifying the impact of the flood environment on pedestrian mobility. Simultaneously, a leader–follower guidance mechanism is integrated to simulate the influence of organized command behavior on group movement. Simulation experiments in typical underground flood scenarios were conducted to validate the proposed model. Simulation results indicate that flood resistance significantly reduces evacuation efficiency, and heterogeneous pedestrian factors such as age distribution also have a considerable impact. The quantitative findings are as follows: flood resistance increased total evacuation time by 9.3% (from 37.5 to 41.0 s) and decreased the average evacuation rate by 8.6%; similarly, raising the proportion of elderly pedestrians from 20% to 30% prolonged total evacuation time by 9.4% and reduced the average evacuation rate by 8.6%. These outcomes verify the effectiveness of the improved model in characterizing heterogeneous pedestrian behavior in underground flooding scenarios. This study provides a more refined theoretical model and simulation tool to support the development of emergency evacuation plans for underground spaces during floods. Full article

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces. Full article

Gases are prone to accumulating in mines. Untimely gas drainage can easily trigger gas outbursts, which may further lead to gas explosions, directly endangering personnel lives and mine safety. Therefore, gas control during gob-side entry driving (roadway excavation adjacent to the goaf) in high-gas mines is crucial to ensuring successful and safe mining and excavation. The 110505 track haulage gateway is a typical high-gas gob-side driving gateway. The measured maximum gas content of the lower No.5 coal seam is 6.0289 m³/t. At present, without a scientific basis for optimizing core parameters, such as the spacing and diameter of gas drainage boreholes, gas drainage is incomplete, and triangular gas pressure zones are likely to form between boreholes. As a result, the risk of gas accumulation is high. This not only exacerbates the danger of unpredicted gas outbursts but also seriously hinders the rapid excavation of the gateway and the progress of mining and further excavation. Based on a mechanical framework coupling coal seam and methane migration, and focusing on the relationships between factors such as borehole spacing, borehole aperture, methane drainage duration, and overall gas drainage efficiency, a model incorporating dual pore distribution and unified permeability characteristics was constructed. Numerical modeling was performed using the COMSOL Multiphysics platform to examine the influences of different borehole spacings and apertures on underground gas drainage in coal seams. The results indicate that reducing borehole spacing contributes to a more pronounced decline in gas pressure and a lower peak pressure between neighboring boreholes. When an interval spacing of 0.3 m was adopted for the drilling layout arrangement, the peak gaseous potential within the surrounding rock matrix dropped to 0.48 MPa following continuous drainage over a duration of 20 days, a reduction of 44%, and there was no obvious triangular zone of pressure. In contrast, borehole diameter had a minor effect on gas drainage efficiency, and the maximum gas pressure after 20 days was less than 0.52 MPa under different borehole diameters. This work establishes a theoretical foundation and offers practical guidance for high-efficiency gas drainage during gob-side entry driving, which is of vital importance for achieving safe and rapid excavation in high-gas mines. Full article

Accurate prediction of pile-cap strain is crucial for the safety of wind-turbine foundations, yet conventional methods struggle to screen key features from high-dimensional monitoring data and to model the nonlinear coupling between endogenous and exogenous variables, hindering both accuracy and interpretability. To address these limitations, this paper proposes a pile-cap-strain prediction method integrating XGBoost-SHAP feature selection with the TimeXer deep-learning model. XGBoost-SHAP first identifies critical predictors from high-dimensional pile-stress data; the TimeXer model then exploits its endogenous–exogenous fusion mechanism for strain prediction. The results show that XGBoost-SHAP effectively selected 10 key features, of which the upper-middle and middle windward-side stresses (Z1-4A, Z1-5A) contributed over 40% of the explanatory power. This stage performs dimensionality reduction and sensor-importance interpretation, halving the input dimensionality while maintaining accuracy comparable to the full 19-channel input. TimeXer achieved a coefficient of determination (R²) of 0.993 in single-step prediction, comparable to the best-performing baselines, and maintained stable performance over a 120 min multi-step horizon. In a zero-shot cross-site transfer test, TimeXer attained the highest eight-step average R² (0.914) among all models, indicating strong cross-site generalization. Attention-mechanism visualization further suggested consistency between the model’s prediction logic and structural mechanics principles. The proposed framework provides a technical solution combining high accuracy with strong interpretability for wind-turbine foundation health monitoring. Full article

At present, research on the long-term stability of multi-cavern coordinated injection–production operations for salt cavern compressed air energy storage (CAES) remains limited. Large-capacity energy storage utilizing multiple interconnected salt caverns has become an inevitable development trend for modern CAES power stations, highlighting the necessity and importance of stability evaluation and design optimization for underground salt cavern storage clusters. Based on the Anning 350 MW CAES demonstration project, this paper takes the abandoned salt caverns of the project as research objects. A three-dimensional geological and cavern model is established using the FLAC3D numerical simulation method, and stability analysis is carried out under static conditions and three long-term gas injection and production scenarios (the pressure conditions are provided by ground-based equipment). The characteristics of the plastic zone, displacement, stress distribution, and volume shrinkage of the caverns are systematically investigated. The results show that under static conditions, the internal pressure significantly controls the development of the plastic zone, and the caverns are generally stable at pressures above 4 MPa. During long-term operation, the plastic zones of each cavern gradually expand, displacements accumulate continuously, and stresses tend to stabilize after an initial accumulation period. After 30 years of operation, no through-going plastic zones appear in any cavern, and all volume shrinkage rates are below 30%. Among the three cases, Case 1 exhibits the best stability, while enhanced monitoring is required for local high-stress regions in Case 3. This study verifies that the salt cavern development for the Anning CAES project is safe and controllable during long-term operation. The layout spacing of caverns is reasonably designed and fully satisfies the stability requirements of salt cavern CAES power stations. The research results can provide a technical guarantee for the construction of the first CAES power station in Yunnan Province and also offer a reliable reference for the design and construction of similar multi-cavity salt cavern CAES projects. Full article

With the rapid expansion of urban underground space in China, anti-floating has become a critical challenge, and uplift piles are a key solution. Previous studies on composite anchor-cable uplift piles have primarily focused on small-diameter single-pipe types (≤600 mm), often simplifying the pile as an integral component, leaving the multi-interface stress transfer mechanisms of large-diameter piles inadequately understood. This study proposes a back-analysis method based on orthogonal experiments, implemented using Abaqus 3D finite element software, to determine interfacial mechanical parameters for three critical contact pairs (strand-grout, grout-steel pipe, steel pipe-concrete) in large-diameter multi-pipe composite anchor-cable uplift piles. These parameters are then implemented in a refined 3D finite element model to simulate the load-deformation behavior of such piles. Quantitative results show that the back-calculated parameters are highly reliable, with maximum simulation errors for pile head displacement limited to 13.0% and 9.6% for fully bonded and semi-bonded piles, respectively. Unlike conventional piles, stress and strain in this new pile type transfer progressively from the inner steel strands outward and from the top downward, resulting in reduced pile-soil displacement mismatch, fuller mobilization of side interfacial strength, and effective mitigation of concrete cracking. This study provides a systematic parameter-calibration framework and numerical platform, offering theoretical and technical support for optimized design and engineering application of large-diameter composite uplift piles. Full article

Autonomous navigation is a key requirement for underground mine automation, where the choice of a suitable global path planner plays a significant role. In this study, four representative planning approaches—Dijkstra’s algorithm, A*, Rapidly exploring Random Tree (RRT*), and Particle Swarm Optimization (PSO)—were evaluated on a differential-drive mobile robot within the ROS navigation framework. The algorithms were tested in two simulated underground environments: a room-and-pillar layout with relatively open space and multiple path alternatives and a narrow tunnel scenario designed to reflect more constrained mining conditions. The results indicate that Dijkstra’s algorithm consistently produced the shortest paths with the lowest computation times, while A* showed comparable performance with slightly higher computational effort. RRT* required modifications to operate effectively in narrow tunnels and exhibited significantly longer planning times. PSO, although capable of generating near-optimal solutions in open spaces, showed limitations in constrained environments due to collision handling and path feasibility issues. Differences in replanning behavior were observed when unknown obstacles were introduced. Overall, graph-based planners such as A* and Dijkstra’s algorithm demonstrated more stable and predictable performance. Future work will focus on validating these findings in real mining environments, particularly considering wheel slippage, sensor noise, and path generation challenges in narrow tunnel conditions. Full article

Slopes transitioning from open-pit to underground mining typically exhibit heterogeneous and nonlinear deformation characteristics. Under complex environmental disturbances, monitoring data are often affected by high noise and outliers, making it difficult to accurately capture critical deformation characteristics and posing challenges for landslide early warning and safety assessment. Therefore, it is necessary to develop a high-precision data optimization technique suitable for complex, high-noise monitoring time series data to improve slope stability evaluation and the robustness of prediction algorithms. Based on slope deformation monitoring data from the Hainan Shilu Iron Mine, the multi-type, nonlinear, and alternating acceleration-deceleration patterns of deformation time series data were analyzed, and the performances of multiple anomaly detection and interpolation compensation algorithms were compared. The results show that the Local Outlier Factor (LOF) and K-Nearest Neighbors (KNN) algorithms achieve better performance in processing noisy and dynamically varying time series data based on comparative evaluations of detection accuracy and interpolation error. Furthermore, a Bessel function-based denoising technique was proposed for landslide monitoring systems. This technique effectively filters high-frequency noise while preserving the main characteristics of the data and outperforms conventional methods, including the Moving Average Method (MAM), Triple Exponential Smoothing (TES), and Least Squares Method (LSM). The proposed technique, integrating LOF-based anomaly detection, KNN-based interpolation compensation, and Bessel function denoising, can effectively process slope deformation monitoring data characterized by multi-type, nonlinear, and alternating acceleration-deceleration patterns. Engineering application at the Hainan Shilu Iron Mine demonstrated that the proposed technique improves data quality and model prediction performance, providing valuable support for slope stability analysis and disaster early warning systems in slopes transitioning from open-pit to underground mining. Full article