Search Results (299)

Close-distance multi-seam mining frequently induces secondary surface deformation and subsidence. Extracting a lower coal seam beneath an existing goaf repeatedly disturbs the overburden, often leading to roof collapse and the expansion of vertical water-conducting fractures that connect the working face to aquifers. Furthermore, the overlying goaf increases the risk of water inrush into active lower workings. This study investigates the mechanisms of strata reactivation and fracturing within an overlying goaf during lower seam extraction at a mine in Northwest China. Using theoretical analysis, numerical simulation, and microseismic monitoring, the research examines the secondary fracture mechanisms of the goaf roof and the resulting water-inrush potential. Research Findings: Strata Instability: Analysis of the key sandstone strata indicates that subsidence (W) of the key rock blocks satisfies 3.17 < W₁ = 4.61 m < 18 m for the lower seam and 3.17 m < W₂ = 5.31 m < 69.6 m for the 3-1# seam. These values confirm that key rock blocks in the basic roof undergo “reactivated” instability following fracture during lower seam mining. Pressure Relief and Fluid Dynamics: Mining-induced fracture initiation and propagation trigger strata reactivation. As the distance to the center of the goaf decreases, the subsidence of the overburden increases, ultimately resulting in a “trapezoidal” bending deformation pattern. Due to secondary activation, the roof subsidence 30 m above the 221 coal seam increased from 1.89 m to 5.475 m. The layers of high-strength, medium-grained sandstone and siltstone overlying the 317 coal seam and beneath the 221 goaf serve as high-strength material for the overlying rock formations. This suppresses the development of the caving zone and fracture zone, leading to subsidence failing to reach the sum of the heights of the two coal seams (6.8 m) and only reaching a value of 5.475 m. During extraction, the stress field undergoes a distinct evolution: it transitions from an initial “regular triangular” pressure-relief zone into a tripartite “weak–strong–strong” distribution. Furthermore, fluid discharge in the overlapping zone between the 317 working face and the 221 goaf increased sequentially, displaying an “alternating” pattern of peak vector variations as the face advanced. Microseismic Activity: Monitoring within the 300–500 m range identified frequent low-energy events and high-magnitude events (10⁴ J, 10⁵ J). These findings demonstrate that secondary excavation directly impacts the aquifer, creating a significant water-inrush hazard for the active working face. Full article

Nonlinear loads are widely applied, making the generation mechanism of grid harmonics increasingly intricate. However, high-precision monitoring devices suffer from high deployment costs and limited coverage. This poses a major challenge to directly acquiring harmonic voltages at some nodes. To solve this problem, this paper proposes a harmonic state estimation method based on a Dual-Stream Adaptive Fusion Generative Adversarial Network (DSAF-GAN), with an innovative design in its generator architecture. A dual-path generator is developed to extract multi-scale features through heterogeneous network branches collaboratively. The ResNet-GRU path integrates convolutional residual modules with Bidirectional Gated Recurrent Units (Bi-GRUs). It effectively captures local spatial patterns and temporal dynamic characteristics of time-series data. The multi-layer perceptron (MLP) path focuses on mining global nonlinear correlations, thereby enhancing the overall feature-expressing capability. An adaptive weight fusion module (Attention Weight Net) fuses the outputs of the two paths. It dynamically allocates contribution weights, improving the model’s flexibility and generalization performance. Experimental results show that the proposed DSAF-GAN can accurately reconstruct the harmonic voltage component content rate of missing nodes. Full article

To address the challenges of strong electromechanical coupling, nonlinear friction, and poor disturbance rejection in semi-direct-drive scraper conveyor systems under complex coal mining conditions, this paper aims to propose a high-performance drive control strategy that balances dynamic response speed with steady-state operational smoothness. First, an integrated electromechanical coupling dynamic model incorporating Permanent Magnet Synchronous Motor (PMSM) vector control and the time-varying meshing stiffness of a two-stage planetary gear train is established. Subsequently, a Sliding Mode Control (SMC) strategy optimized with a saturation boundary layer is designed and compared with traditional Proportional-Integral (PI) control under multiple operating conditions. Time-frequency domain analysis indicates that SMC significantly enhances the dynamic stiffness of the drive system. Under sudden load change conditions, the speed recovery time is shortened by approximately 76%, and the steady-state error is reduced by 37% compared to PI control. Microscopic characteristic evaluation based on FFT and Total Variation (TV) metrics reveals that SMC achieves active disturbance rejection through spectral broadening of the electromagnetic torque. Crucially, the steady-state cumulative control effort of SMC is equivalent to that of PI, implying no additional mechanical stress burden, while the equivalent dynamic transmission force fluctuation in the mechanical chain is reduced by about 3%. The study confirms that the proposed strategy successfully achieves a synergistic optimization of “macroscopic rapid response” and “microscopic smooth operation,” providing a theoretical basis for the high-precision control of heavy-duty underground transmission equipment. Full article

Factor of Safety (FOS) is a significant index to measure the stability condition of a rock slope in mining or civil engineering. In this paper, we evaluate and compare four different machine learning models, Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), Random Forest (RF), and a hybrid genetic algorithm–multi-layer perceptron (GA-MLP), using two separate real-world datasets. The two separate datasets used in this study are from a previously conducted study on highway excavation with rock cutting in China, and another one in a mining site in Peru, with five geotechnical properties used as inputs, including slope height, slope angle, unit weight, cohesion, and friction angle. The two separate datasets were separated into training, validation, and testing datasets. The testing dataset of the models is unseen data used to assess model performance in an unbiased manner. The result shows that the SVR had the highest prediction accuracy, followed by GPR for the mining dataset, and GPR had the highest performance among all the models for the highway excavation dataset. From the boxplot, we can see that SVR, while having the highest predictive accuracy, has a larger variance in prediction compared to GPR for the mining dataset. Full article

Coal and rock recognition is one of the key technologies in mining production, but traditional methods have limitations such as single-feature representation dimension, insufficient robustness, and unbalanced performance in lightweight design under noise interference and complex feature conditions. To address these issues, an Efficient Channel Attention Reparameterized Network (ECA-RepNet) based on recurrence plot and Efficient Channel Attention mechanism is proposed. The one-dimensional vibration signal is mapped to the two-dimensional image space through a recurrence plot (RP), which retains the dynamic characteristics of the time series while capturing the complex patterns in the signal. Multi-scale feature extraction and lightweight design are achieved through the reparameterized large kernel block (RepLK Block) and the depthwise separable convolution (DSConv) module. The ECA module is introduced to embed multiple convolutional layers. Through global average pooling, one-dimensional convolution, and dynamic weight allocation, the modeling ability of inter-channel dependencies is enhanced, the model robustness is improved, and the computational overhead is reduced. Experimental results demonstrate that the ECA-RepNet model achieves 97.33% accuracy, outperforming classic models including ResNet, CNN, and MobileNet in parameter efficiency, training time, and inference speed. Full article

For mine ventilation-on-demand (VOD) scenarios, conventional joint optimisation of airflow augmentation and energy saving in mine ventilation systems is often constrained in practical engineering applications by shrinkage of the feasible region, limited adjustable resistance margins, and strongly multi-modal objective functions. These factors tend to result in low solution efficiency, pronounced sensitivity to initial values and insufficient solution robustness. In response to these challenges, a two-layer intelligent evolutionary optimisation framework, termed ES–Hybrid JADE with Competitive Niching, is developed in this study. In the outer layer, four classes of evolutionary algorithms—CMAES, DE, ES, and GA—are comparatively assessed over 50 repeated test runs, with a combined ranking based on convergence speed and solution quality adopted as the evaluation metric. ES, with a rank_mean of 2.0, is ultimately selected as the global hyper-parameter self-adaptive regulator. In the inner layer, four algorithms—COBYLA, JADE, PSO and TPE—are compared. The results indicate that JADE achieves the best overall performance in terms of terminal objective value, multi-dimensional performance trade-offs and robustness across random seeds. Furthermore, all four inner-layer algorithms attain feasible solutions with a success rate of 1.0 under the prescribed constraints, thereby ensuring that the entire optimisation process remains within the feasible domain. The proposed framework is applied to an exhaust-type dual-fan ventilation system in a coal mine in Shaanxi Province as an engineering case study. By integrating GA-based automatic ventilation network drawing (longest-path/connected-path) with roadway sensitivity analysis and maximum resistance increment assessment, two solution schemes—direct optimisation and composite optimisation—are constructed and compared. The results show that, within the airflow augmentation interval [0.40, 0.55], the two schemes are essentially equivalent in terms of the optimal augmentation effect, whereas the computation time of the composite optimisation scheme is reduced significantly from approximately 29 min to about 13 s, and a set of multi-modal elite solutions can be provided to support dispatch and decision-making. Under global constraints, a maximum achievable airflow increment of approximately 0.66 m³·s⁻¹ is obtained for branch 10, and optimal dual-branch and triple-branch cooperative augmentation combinations, together with the corresponding power projections, are further derived. To the best of our knowledge, prior VOD airflow-augmentation studies have not combined feasibility-region contraction (via sensitivity- and resistance-margin gating) with a two-layer ES-tuned JADE optimiser equipped with Competitive Niching to output multiple feasible optima. This work provides new insight that the constrained airflow-augmentation problem is intrinsically multimodal, and that retaining multiple basins of attraction yields dispatch-ready elite solutions while achieving orders-of-magnitude runtime reduction through prediction-based constraints. The study demonstrates that the proposed two-layer intelligent evolutionary framework combines fast convergence with high solution stability under strict feasibility constraints, and can be employed as an engineering algorithmic core for energy-efficiency co-ordination in mine VOD control. Full article

Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article

To address the high-dilution issues arising from efficient mining methods such as medium-deep drilling for underground thin veins of precious metals, detecting raw rock fragments after blasting for subsequent sorting has become a cutting-edge research focus. With the continuous advancement of artificial intelligence, deep learning offers novel applications for rock detection. Accordingly, this study employs an improved lightweight YOLOv8n model to detect two typical thin-vein precious metal ores: gold ore and wolframite. In consideration of the computational resource constraints in underground environments, a triple optimization strategy is proposed. First, GhostConv and C2f-Ghost modules were introduced into the backbone network to reduce redundant computations while preserving feature representation capabilities. Second, the VoVGSCSP module was incorporated into the neck to further decrease model parameters and computational load. Finally, the ECA mechanism was embedded before the SPPF pooling layer to enhance feature extraction for ores and rocks, thereby improving detection accuracy. The results demonstrate that the GVE-YOLOv8 model contains only 2.28 million parameters—a 24.3% reduction compared to the original YOLOv8n. FLOPs decrease from 8.1 G to 5.6 G, and the model size reduces from 6.3 MB to 4.9 MB, while detection accuracy improves to 98.3% mAP50 and 95.3% mAP50-95. This enhanced model meets the performance requirements for accurately detecting raw ore and rock fragments after underground blasting, thereby providing a novel research method for thin-vein mining. Full article

The repeated extraction of coal seams in the Loess Plateau mining region has greatly increased the severity of surface deformation and associated damage. Accurately characterizing the spatio-temporal evolution of subsidence and the underlying mechanisms is a critical engineering challenge for mining safety. Taking the Dafosi Coal Mine located in the loess gully region as a case study, this paper thoroughly examines the variations in surface deformation and damage characteristics caused by single and repeated seam mining. The analysis integrates surface movement monitoring data, global navigation satellite system (GNSS) dynamic observations, field surveys, unmanned aerial vehicle (UAV) photogrammetry, and numerical simulation methods. Notably, to ensure the accuracy of prediction parameters, a refined Particle Swarm Optimization (PSO) algorithm incorporating a neighborhood-based mechanism was employed specifically for the inversion of probability integral parameters. The results indicate that the subsidence factor and horizontal movement factor increase markedly following repeated mining. The maximum surface subsidence velocity also increases substantially, and this acceleration remains evident after normalizing by mining thickness and face-advance rate. The fore effective angle is smaller in repeated mining than in single-seam mining, and the duration of surface movement is substantially extended. Repeated mining fractured key strata and caused a functional transition from the classic three-zone response to a two-zone connectivity pattern, while the thick loess cover responds as a disturbed discontinuous-deformation layer, which together aggravates step-like and slope-related damage. The severity of surface damage is strongly influenced by topographic features and geotechnical properties. These findings demonstrate that the proposed integrated approach is highly effective for geological hazard assessment and provides a practical reference for engineering applications in similar complex terrains. Full article

Degraded post-mining landscapes require reclamation strategies that ensure soil stability, environmental safety and successful vegetation establishment. This study evaluated two soil cover systems applied between 2020 and 2025 on a mining spoil heap in Libiąż, Poland: a two-layer (TL) cover with a soil substitute layer and a multilayer (ML) cover incorporating additional insulating materials. Both covers were non-saline and mildly alkaline. The applied methods supported favorable soil conditions after five years, with stable organic matter (24.48–28.26%), nitrogen (4.5–4.9 g/kg) and phosphorus (1.5–1.6 g/kg) contents, while potassium decreased markedly (from 17.1 to 6.44–6.83 g/kg), likely due to plant uptake or leaching. Leachate analyses showed low concentrations of toxic metals and salinity-related ions, confirming the environmental safety and inert properties of the soil substitute. Vegetation assessments revealed differences between reclamation systems, with Phragmites australis exhibiting greater stalk length, plant density and biomass in the TL cover. Establishment costs were also substantially lower for TL (EUR 1.65/m²) than for ML (EUR 6.14/m²). These results indicate that soil substitute covers provide a safe, cost-effective and functionally efficient reclamation option that supports circular economy principles by reusing mining waste and coal combustion by-products, while Phragmites australis enhances vegetation development and overall reclamation success. Full article

In smart manufacturing environments, production scheduling is highly susceptible to multi-source disruptions. However, traditional methods often struggle to accurately characterize the complex interdependencies between control points and disruptions, along with their systemic propagation effects, thereby constraining the proactivity and precision of scheduling optimization. This paper proposes a novel data-driven approach that integrates Weighted Association Rule Mining (WARM) with a two-layer directed weighted complex network to achieve precise identification of critical control points in production scheduling. First, a production loss function integrating delay duration and resource idle cost is constructed, and the max-pooling method is applied to map control point weights, thereby quantifying their intrinsic importance. Subsequently, under the constraint that association rule antecedents are restricted to control points, an improved Apriori algorithm is employed to mine directed “Control Point-Disruption” association rules. These rules are then used to construct a two-layer directed weighted complex network. Furthermore, by combining weighted PageRank and edge betweenness centrality analyses, critical control points and high-risk propagation paths are identified from the dual dimensions of node influence and path propagation capability. A case study conducted in a crankshaft production workshop demonstrates that the proposed method effectively identifies low-frequency yet high-impact hidden nodes often overlooked by traditional rules. The resulting scheduling optimization scheme reduces the occurrence rate of high-impact disruptions by 53% and significantly improves key performance indicators such as on-time delivery rate and equipment utilization. This research provides new theoretical support and a technical pathway for manufacturing enterprises to suppress system disturbances through flexible interventions targeting high-betweenness paths. 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 article is devoted to the issues of designing the exploitation of a seam deposit in the boundary areas of underground mines in terms of minimizing the risk of dynamic phenomena. Its main goal was to attempt to demonstrate the relationship between the method of extracting resources trapped in the boundary pillar and the magnitude of the induced seismicity of the rock mass accompanying this process. The substantive considerations concerned the single-wall model and were divided into two main parts—theoretical and verification. As part of the theoretical piece, based on model studies, a geomechanical assessment of the impact of the working face advance on changes in the stress–strain behaviour occurring in the burst-prone layer in terms of the possible loss of continuity of its original structure was carried out. The starting point for the key analyses were the results of numerical simulations based on the algorithms of S. Knothe and W. Budryk’s theories in combination with classical solutions of the mechanics of deformable bodies. Two variants of mining operations in a two-sided environment of goaf were considered, differing in the direction of progress, the degree of constraint of the start and end of the face advance, and mining circumstances in the vicinity of both sides of the advancing face. As part of the verification piece, the results of model analyses were related to an example polygon of a crossing longwall in one of the functioning, rockburst USCB hard coal mines. The scope of the research included a comparison of the experimentally indicated zones of occurrence of tremor-favourable effort processes in the roof of the seam with the actual location of the seismic phenomena foci recorded during the ongoing exploitation. The considerations included in the work formed the basis for formulating conclusions of a cognitive and applicable nature. Full article

This study investigates the influence of shallow water-bearing sand layers on surface subsidence characteristics in coal mining areas with thick loose strata, with the ultimate goal of contributing to sustainable environmental protection. Firstly, a numerical simulation test was designed to analyze and study the influence of the loose layer thickness, mining height, bedrock slope, and sand inclusion on the surface movement and deformation characteristics. Secondly, the mechanical model of seepage flow in the sand layer was established to study the influence mechanism of the internal stress distribution of the sand layer and the seepage of the water body after mining on the surface subsidence. Finally, by studying the law of surface subsidence corresponding to the mining of 3205 working face in a mine, it was found that mining caused the partial overlying soil layer to move integrally and generate a large displacement difference with the adjacent layer, which verifies the conclusions of numerical simulation and mechanical analysis. The results of the study show that the thickness of the loose layer is the main control factor that causes the surface subsidence range and the building damage to increase; the shallow water-bearing sand-bearing layer has two types of movements: displacement and flow. The critical hydraulic slope has not reached the sand. The layer has a linearly increasing horizontal displacement value in the thickness direction; when the critical hydraulic slope is reached, the sand layer cannot transmit the frictional force, causing the overlying soil layer to slide as a whole. Both forms are prone to tensile damage on the surface. The research results provide a theoretical basis and practical case for surface subsidence reduction and green mining under similar geological conditions. Full article

This paper focuses on the control problems of a multi-modal underwater robot, which is designed mainly for the task of detecting the working environment in deep-sea mining. To tackle model uncertainty and external disturbances, an improved linear active disturbance rejection control scheme based on sliding mode control is proposed (SM-ADRC). Firstly, to reduce overshoot, a piecewise $fhan$ function is introduced into the tracking differentiator (TD). This design retains the system’s fast nonlinear tracking characteristics outside the boundary layer while leveraging linear damping within it to achieve effective overshoot suppression. Secondly, two key enhancements are made to the SMC: an integral sliding surface is designed to improve steady-state accuracy, and a saturation function replaces the sign function to suppress high-frequency chattering. Furthermore, the SMC integrates the total disturbance estimate from the linear extended state observer (LESO) for feedforward compensation. Finally, the simulation experiment verification is completed. The simulation results show that the SM-ADRC scheme significantly improves the dynamic response and disturbance suppression ability of the system and simultaneously suppresses the chattering problem of SMC. Full article