Search Results (2,041)

In underground coal mining, the gear of a shearer’s rocker arm endures extreme stress and environmental fluctuations. Failures in this vital component can pose serious safety hazards, cause prolonged operational downtime, and result in significant financial losses. Therefore, accurate gear fault diagnosis is crucial. However, conventional diagnostic methods often struggle with limited feature extraction and poor performance when dealing with non-stationary, noisy signals typical of this environment. To address these challenges, a hybrid model consisting of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Markov Transition Model (MTM) is proposed. In this framework, the CNN is used to extract both global and local features related to gear fault. A time-distributed feature extractor is then integrated with the LSTM to capture the temporal progression of these features, aiding in effective modeling of fault evolution over time. Finally, the MTM further refines classification by incorporating probabilistic state transition between fault conditions, thereby improving diagnostic stability and robustness under noise. Experimental validation was done using vibration data from the Taizhong Coal Machinery rocker arm test platform and gear data from Southeast University and achieved up to 99.79% accuracy. These results show this proposed method outperformed other advanced diagnostic methods, offering dependable fault diagnosis and strong noise resistance even under extreme noise conditions of −5 dB SNR. Full article

Fiber-reinforced cemented tailings backfill (FTB) has been widely adopted in underground mining operations as an effective solution for mitigating the brittleness of cemented tailings backfill (CTB) and ensuring compatibility with deep mining environments. Understanding the coupled effects of temperature and binder composition on the thermal–hydro–mechanical–chemical (THMC) behavior of FTB is essential for low-carbon mix design and practical application. To address this knowledge gap, this work presents a systematic investigation into the influences of curing temperature, binder type, and cement content on the rheological properties, compressive strength, and THMC-related parameters of FTB. The results demonstrate that elevated temperatures accelerate hydration, reducing flowability while significantly enhancing strength and pore structure refinement. Conversely, low temperatures preserve flowability but impede strength development. The incorporation of slag or fly ash as partial cement substitutes reduces rheological parameters; however, fly ash substitution tends to compromise ultimate strength. Multi-field performance monitoring further reveals the underlying coupling mechanisms among temperature evolution, hydration kinetics, matric suction, and mechanical strength development. Based on these insights, a low-carbon design strategy is proposed, emphasizing dynamic optimization of cement content according to ambient temperature. These findings offer a theoretical foundation for the sustainable proportioning and performance control of mine backfill materials. Full article

Many challenges are commonly encountered in the underground mining of steeply dipping thin-to-medium-thick orebodies associated with weak hanging wall rockmass, such as stope back collapse, high ore dilution, and poor stoping stability. To address these issues, a synergistic mining method combining sidewall retaining and open stoping with a delayed backfilling method is proposed. Taking the north wing orebody of the Erlihe lead–zinc mine as the engineering background, a 3D finite element numerical simulation model was established using MIDAS GTS(2026 version) to conduct a comparative analysis between the proposed mining method and the current mining method. The mechanical response characteristics of crown pillar stress, crown pillar settlement, hanging wall displacement, and plastic zone evolution were systematically investigated under different mining stages. The results show that the proposed method improves the stress and deformation distribution at the bottom of the crown pillar. The peak stress decreases from 13.72 MPa to 12.86 MPa, and the spatial extent of the high-stress zone is noticeably reduced. Meanwhile, the maximum crown pillar subsidence decreases, while the width of the main subsidence zone decreases from 11 nodes to 9 nodes, and the settlement of the end region decreases by 6.05%. In terms of hanging wall response, the maximum displacement is reduced by 9.3–26.5% during the stope extraction stage and 9.6–10.0% during the inter-pillar recovery stage, with an overall average reduction of approximately 14.0%. Furthermore, the plastic zone in the hanging wall surrounding rock becomes smaller and develops later under the proposed mining method. Our findings demonstrate that the new proposed mining method effectively modifies the stress transfer path, mitigates deformation of both the crown pillar and hanging wall rock, and delays the development of plastic failure, thereby improving stope stability under weak hanging wall rockmass conditions. The proposed method provides a practical technical solution for the safe and efficient extraction of steeply dipping thin-to-medium-thick orebodies. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

To investigate the structural characteristics of Ir_n clusters (n = 9–30) and their interaction with NH₃, the CALYPSO structure-prediction method was employed to identify the lowest-energy configurations. The Lennard–Jones potential was then used to compute the binding energy and average binding energy, thereby evaluating size-dependent stability. The results show that Ir_n clusters evolve from relatively open motifs to compact three-dimensional frameworks as n increases. Meanwhile, the average binding energy increases overall and exhibits several locally stable size regions, indicating a pronounced size effect. Based on slab and cluster models, NH₃ adsorption was further examined on the Ir₁₃ cluster as a representative system due to its high structural stability as a “magic-number” cluster. The calculated adsorption energies demonstrate that the Ir₁₃ cluster exhibits substantially stronger adsorption than the bulk Ir surface, with low-coordinated Ir atoms playing a key role in strengthening the interaction and enhancing adsorption activity. Adsorption-configuration analysis indicates that NH₃ preferentially binds to active surface sites via the N lone pair. These findings clarify the relationship between structural stability and adsorption performance of Ir clusters and provide theoretical support for Ir-based materials in NH₃ catalytic conversion and high-sensitivity gas detection, and offer insights relevant to improving NH₃ monitoring in underground coal mine environments. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

In response to the need to monitor groundwater migration and structural damage to rock strata during tunnel excavation and coal mining, this paper presents a novel electromagnetic detection system that features continuous ground-based transmission and full-waveform underground observation. As the transmitted waveform is crucial for determining the distribution of induced eddy currents and the characteristics of the secondary field response, studying these response characteristics is essential for the system’s practical application. This study selects four typical transmission waveforms—step, triangular, half-sine and trapezoidal—and uses a tetrahedral, three-dimensional grid discretization method to analyze the transient electromagnetic full-wave response patterns of electrical and magnetic sources under different waveform excitations. This elucidates the propagation characteristics of electromagnetic fields in the medium. The research reveals that the waveform type during energization significantly influences the electromagnetic response, with the full-wave response characteristics of electrical and magnetic sources differing significantly in the near-source region and response trends converging in the far-source region. In practical detection, combining the advantages of the three-component responses of the electrical and magnetic sources can effectively improve detection accuracy. The findings of this study provide important theoretical support for optimizing the design of transient electromagnetic detection systems and precisely interpreting detection data. They also lay a theoretical foundation for electromagnetic detection applications in fields such as mineral resource exploration and engineering geological surveys. Full article

The Draa Sfar polymetallic mine, located near Marrakech in Morocco, represents the deepest currently operating underground mine in North Africa, with workings extending beyond depths of −1200 m. At such depths, mining activities are conducted within weak, highly anisotropic foliated black pelites, where recurrent instability mechanisms, most notably rib buckling and crown deterioration, are frequently observed, especially in drifts developed parallel to the foliation planes. In this context, the present study integrates detailed structural field observations with two-dimensional finite-element modelling using RS2 in order to analyse excavation-scale stability within these schistose pelitic rocks. Both numerical simulations and field evidence indicate that increasing depth-related confinement, together with a dominant in situ stress regime, favours stress channelling and localized damage development, while the pronounced transverse weakness of the pelites exerts a primary control on failure kinematics, including schistosity-parallel spalling, asymmetric rib buckling, and shear along inclined foliation intersecting the excavation back. Instability processes are further intensified by excavation geometry and mine layout: angular, square-shaped profiles and foliation-parallel drift orientations generate steeper stress gradients and greater convergence compared to arched sections, while proximity to stopes and adjacent openings enhances mining-induced stress redistribution and associated deformation. Intersection areas emerge as the most critical configurations, where the superposition of stress perturbations and structurally controlled damage mechanisms accelerates wall convergence and roof sagging. Overall, these findings demonstrate that drift stability cannot be adequately evaluated using generic design criteria when excavation geometry, interaction effects, and structural anisotropy exert a dominant influence on mechanical behaviour. Consequently, a fully integrated approach that combines drift geometry optimisation, detailed structural mapping, site-calibrated numerical modelling, and in situ monitoring is required to achieve reliable stability assessment and control. Full article

Currently, deep mining has become the development trend of underground mines, and the harsh working environment underground seriously affects the efficiency of personnel and equipment operations. The operational efficiency of the human–machine system composed of personnel and equipment is not only affected by the status of personnel and equipment, but also closely related to the interaction between human–machine–environment. How to ensure the efficient operation of human–machine systems has become the key to improving the quality and efficiency of mines. Therefore, in order to analyze the interaction relationship between human–machine–environment in the process of human–machine system operation and explore the variation law of human–machine system efficiency. This paper constructs a deep mining human–machine system efficiency system dynamics model under the multi-factor coupling effect of deep well mining, guided by system dynamics theory, and obtains the variation laws of system efficiency under single-factor changes and multi-factor coupling effects. The research results solve the problem of difficulty in quantitatively describing the logical and quantitative relationships between various elements in the study of human–machine system efficiency, providing new ideas for the study of underground work efficiency. Through mathematical modeling, the temperature threshold for the efficient operation of the human–machine system is determined, and the quantitative relationships among temperature, humidity, and wind speed are elaborated, providing a reference for ensuring the efficient operation of the human–machine system in deep mining. Full article

Future lunar mining missions are expected to involve deeper geological conditions. Understanding the mechanical behaviors of the lunar subsurface regolith is essential to operational safety. Recent findings from the Chang’e-4 and Chang’e-5 missions revealed a marked increase in particle size and relative density of lunar regolith with depth. In addition, the geostatic stress naturally increases with depth. These three variables pose significant challenges for accurately predicting the shear strength. Existing predictive models, such as the Alshibli model, fail to account for the distinct conditions of lunar subsurface regolith. To address this, consolidated drained triaxial tests were conducted on the CUMT-1 lunar regolith simulants. The influences of confining pressure, relative density, and particle size distribution on shear strength were systematically analyzed. A novel indicator, named inter-particle void ratio, was introduced to capture the combined effects of relative density and particle size distribution. Based on this indicator, a new empirical model was proposed for predicting peak shear strength under varying subsurface conditions. The results suggest that deeper lunar regolith may have significantly lower shear strength than previously estimated, primarily due to the combined effect of increased inter-particle void ratio and geostatic stress. This finding has important implications for the assessment of excavation efficiency, underground construction stability, and the overall safety of lunar subsurface infrastructure. Full article

The safety and stability of underground coal mining are largely determined by the structural features of coal seams and surrounding rocks. Geological heterogeneities such as faults, fracture zones, and lithological variations strongly influence the distribution of rock pressure and the occurrence of geodynamic hazards. This highlights the need for reliable geophysical methods capable of identifying such zones under mining conditions. Electrical prospecting represents a promising diagnostic approach, as it is highly sensitive to changes in the physical properties of rocks. Unlike conventional geological mapping, it enables the detection of hidden structures and weakened zones often invisible to direct observation. Advances in instrumentation and data processing have further expanded the applicability of electrical methods in complex environments. This study introduces a methodology of electrical prospecting observations for the diagnosis of coal seams. The analysis focuses on conductivity anomalies that reflect tectonic disturbances, fracture systems, and lithological heterogeneities. Field investigations demonstrated the sensitivity of the method to local environmental variations. Comparison with geological records confirmed the validity of the approach: the identified anomalous zones correlated well with documented tectonic features. The methodology showed a stable performance and revealed potential for integration into mine monitoring systems. It allows the identification of areas associated with elevated rock pressure and possible geodynamic activity, thereby contributing to safer underground operations. In the longer term, electrical prospecting may be applied to other coal deposits, including those with a high gas content and complex structure. The development of automated interpretation tools and machine learning algorithms could further increase processing efficiency and improve predictive reliability. Overall, the results confirm that electrical prospecting in mining environments can become an effective instrument for enhancing safety and building more accurate geological–geophysical models of coal seams. Full article

As a primary associated by-product of coal mining, the comprehensive utilization of coal gangue has become a core pathway for the green transformation of the energy system and the establishment of a resource recycling system. The fully mechanized top-coal caving mining method used in China lacks a quality linkage mechanism between underground matched mining and surface coal blending, resulting in significant fluctuations in coal quality, larger volumes of gangue brought to the surface, and low utilization rates of coal washing by-products. In this paper, we propose a reverse decision-making method for whole-lifecycle coal quality control and construct an ash content tracing and regulation model to coordinate coal blending and matched mining in fully mechanized caving faces. In the coal blending stage, under the constraints of calorific value balance and cost minimization, the method transforms low-calorific-value by-products, such as middlings and fine gangue, into valuable resources. In the matched mining stage, a reverse tracking model based on the surface–underground ash content balance is proposed, grounded in material flow analysis theory. The model formulates correlation equations among face length, the low calorific value of raw coal, daily advance per cycle, and caved gangue volume. It further proposes a reverse coal quality tracing theory that links commercial coal sales targets with caving process parameters. The study clarifies the deep coordination mechanism between underground matched mining and surface coal blending. The results demonstrate that the proposed method systematically establishes a closed-loop pathway integrating underground gangue reduction at the source and surface fine gangue blending. The implementation has yielded direct economic benefits totaling RMB 65.31 million, increased commercial blended coal output by 104.5 thousand tons, and reduced gangue emissions by 258.5 thousand tons. This study provides a reference for the reduction, resource utilization, and recycling of coal gangue. Full article

Post-mining surface uplift has affected the northeastern part of Pécs, Hungary, since the closure of underground coal mines in the 1990s. This study synthesises 30 years of SAR data (ERS, Envisat, and Sentinel-1) with geodetic surveys, groundwater monitoring, and over 900 residential damage reports to investigate the spatiotemporal evolution of this deformation. In densely built urban environments, Persistent Scatterer Interferometry (PS-InSAR) provides spatially detailed complementary data measurements to traditional levelling, particularly where survey lines offer limited coverage. The performed combined analysis tracked deformation from initial uplift through stabilisation, revealing a clear transition: while early lower-order measurements showed limited correlation, modern Sentinel-1 data and high-order geodetic surveys (post-2014) demonstrate a robust correlation (R = 0.65). The cross-correlation of InSAR results with geodetic and hydrogeological records revealed that aquifer recovery by the 2010s coincided with the onset of surface stability. While over 90% of 1990s residential damage claims fell within measured deformation zones, this relationship weakened over time, with recent claims showing little spatial connection with ground movements. This highlights the complementary strengths of InSAR and geodetic techniques. It demonstrates the value of integrating geotechnical and socio-economic datasets, providing a transferable framework for reliable deformation monitoring and risk management in post-mining urban environments. Full article

Abandoned mining areas provide valuable opportunities to investigate ore-forming processes, supergene mineral transformations, and the geochemical behaviour of metals. In this sense, the old Preguiça mine (Beja, Portugal), exploited for Fe–Zn–Pb, was studied providing new mineralogical and geochemical data aimed at improving the understanding of the secondary mineral assemblages of this deposit. A total of 70 samples collected from three accessible underground levels (first, second and third) and mine waste, complemented by 16 samples from a deeper level (fourth) previously collected, were analysed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and a portable X-ray fluorescence (pXRF) equipment. Mineralogical phases are dominated by a wide range of secondary oxides, carbonates, arsenates, vanadates, silicates, phosphates and sulphates, but remnants of primary sulphides were also found. The following minerals can be emphasised: goethite, hematite, calcite, dolomite, descloizite, willemite, mimetite, cerussite, smithsonite and fraipontite. The presence of massicot in the Preguiça mine, is described for the first time. Bulk geochemical analyses show high concentrations of Fe, Ca, Zn and Pb, consistent with the observed mineralogy. The presence of vanadium- and arsenic-bearing minerals highlights the occurrence of critical raw materials, supporting the importance of reassessing other abandoned mining areas in the context of sustainable resource management and strategic raw-material planning. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article