Search Results (178)

This study develops an interpretable and calibrated XGBoost framework for probabilistic slope stability assessment using a 627-case database of circular-mode failures. Six predictors, namely, unit weight (γ), cohesion (c), friction angle (φ), slope angle (β), slope height (H), and pore-pressure ratio (rᵤ), were used to train a gradient-boosted tree model optimized through Bayesian hyperparameter search with five-fold stratified cross-validation. Physically based monotone constraints ensured that failure probability (P_f) decreases as c and φ increase and increases with β, H, and rᵤ. The final model achieved strong performance (AUC = 0.88, Accuracy = 0.80, MCC = 0.61) and reliable calibration, confirmed by a Brier score of 0.14 and ECE/MCE of 0.10/0.19. A 1000-iteration bootstrap quantified both epistemic and aleatoric uncertainties, providing 95% confidence bands for P_f-feature curves. SHAP analysis validated physically consistent influence rankings (φ > H ≈ c > β > γ > rᵤ). Predicted probabilities were classified into Low (P_f < 0.01), Medium (0.01 ≤ P_f ≤ 0.10), and High (P_f > 0.10) risk levels according to geotechnical reliability practices. The proposed framework integrates calibration, uncertainty quantification, and interpretability into a comprehensive, auditable workflow, supporting transparent and risk-informed slope management for infrastructure, mining, and renewable energy projects. Full article

With the gradual increase in coal production capacity, the problem of water damage from the coal seam roof is becoming more and more prominent. Neogene loose strata overlie coal seams in eastern China, and pressurized aquifers commonly lie at the bottom of the loose strata. The aquifers are mainly composed of unconsolidated sand, gravel, and weakly consolidated marl, which has strong permeability and an extremely unfavorable impact on safe production. Identifying the target area to prevent and control roof water damage can reduce the likelihood of water damage accidents in mines. This study takes the 8₅ mining district of Wobei mine as an engineering case. The discriminant indexes are selected for aquifer thickness, gradation coefficient, marlstone thickness, permeability, grouting quantity, and grouting termination pressure. A model integrating the newly proposed Crowned Porcupine Optimization (CPO, 2024), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) was constructed to predict unit water influx. A zonal map was generated based on the expected unit water influx of the fourth aquifer after grouting. In addition, the prediction results are compared with those from other models. Results indicate that the CPO-CNN-BiLSTM model achieves a higher accuracy and fewer errors in water abundance prediction, with an RMSE of 2.58 × 10⁻⁵ and an R² of 0.982 for the testing dataset. According to the prediction result, the fourth aquifer after grouting in the 8₅ mining district is divided into five water abundance zones. The strong and medium–strong water abundance zones are mainly distributed in the study area’s eastern region. A small portion of them is distributed in the northwestern and northern areas. This study provides a new insight for predicting the water abundance of thick loose aquifers and a theoretical basis for safe mining under thick loose aquifers. Full article

Permeability evolution is one of the key parameters influencing the efficient exploitation of deep unconventional energy resources, as it reflects the dynamic development of pore-fracture structures under complex engineering effects. Using fractal geometry to describe the pore-fracture system, rock permeability enhancement can be quantitatively evaluated. In this study, fractured coal specimens were analyzed under simulated mining-induced stress relief and CH₄ release conditions based on fractal geometry theory. The permeability-enhancement rate was derived and verified through CT (Computed Tomography) characterization of the pore-fracture network. The fractal dimension of the fracture aperture distribution and the tortuosity of fracture paths were determined to establish a fractal permeability-enhancement model, and its sensitivity was analyzed. The results indicate that permeability evolution undergoes four distinct stages: a stable stage, a slow-growth stage, a rapid-growth stage, and a stable or declining stage. The mining-induced stress relief and gas desorption effects significantly accelerate permeability enhancement, providing new insights into the mechanisms governing gas flow and pressure relief in deep coal seams. The proposed model, highly sensitive to the fracture aperture ratio (λ_min/λ_max), reveals that a smaller aperture span leads to greater permeability enhancement during the damage and fracture stage. These findings offer practical guidance for predicting permeability evolution, optimizing gas drainage design, and enhancing the safety and efficiency of coal mining and methane extraction operations. Full article

Industrial processes governed by food safety regulations, such as high-temperature short-time (HTST) pasteurization, rely on continuous sensor monitoring to ensure compliance with standards like Hazard Analysis and Critical Control Points (HACCP). However, extracting actionable process insights from raw sensor data remains a non-trivial task, largely due to the continuous, multivariate, and often high-frequency characteristics of the signals, which can obscure clear activity boundaries and introduce significant variability in temporal patterns. This paper proposes a process mining framework to extract activity-based representations from multivariate sensor data in a pasteurization scenario. By modelling temperature, pH, conductivity, viscosity, turbidity, flow, and pressure signals, the approach segments continuous data into discrete operational phases and generates event logs aligned with domain semantics. Unsupervised learning techniques, including Hidden Markov Models (HMMs), are used to infer latent process stages, while domain knowledge guides their interpretation in accordance with critical control points (CCPs). The extracted models support conformance checking against HACCP-based procedures and enable predictive process-monitoring tasks such as next-activity prediction and remaining time estimation. Experimental results on synthetic (literature-grounded data) demonstrated the method’s ability to enhance safety, compliance, and operational efficiency. This study illustrates how integrating process mining with regulatory principles can bridge the gap between continuous sensor streams and structured process analysis in food manufacturing. Full article

The escalating environmental crisis and the threat posed by environmental crime demand more effective prevention strategies. The predictive mapping of environmental crimes can address this challenge by improving monitoring and response. This study proposes an analysis and modelling of the occurrence of environmental crimes in the Canary Islands, a territory of exceptional ecological value and strong tourism and urban sprawl pressures. Four types of illegal activity were examined: buildings and constructions, mining and tilling, solid waste dumping, and liquid waste discharging. A predictive modelling framework based on Random Forest (RF) machine learning algorithms was applied to identify spatial patterns and environmental crime potential. A colour-based environmental crime potential map was generated for each island, showing the likelihood of 0, 1, 2, 3, or all 4 types of environmental crime. Findings reveal that 43.2% of the surface area of the islands could potentially be affected by at least one crime type. Potential occurrences are lower in protected natural areas, in islands with lower population densities and in inland areas compared to coastal regions. The methodology provides a foundation for future research which could assist policymakers and environmental protectors in combating and preventing environmental crimes more effectively and contribute to the preservation of their ecosystems. Full article

Diesel engines power much of the heavy-duty equipment used in underground mines, where exhaust emissions pose acute environmental and occupational health challenges. However, predicting the amount of air required to dilute these emissions is difficult because exhaust mass flow and pollutant concentrations vary nonlinearly with multiple operating parameters. We apply deep learning to predict the total exhaust mass flow and carbon monoxide (CO) concentration of a six-cylinder gas–diesel (dual-fuel) turbocharged KAMAZ 910.12-450 engine under controlled operating conditions. We trained artificial neural networks on the preprocessed experimental dataset to capture nonlinear relationships between engine inputs and exhaust responses. Model interpretation with Shapley additive explanations (SHAP) identifies torque, speed, and boost pressure as dominant drivers of exhaust mass flow, and catalyst pressure, EGR rate, and boost pressure as primary contributors to CO concentration. In addition, symbolic regression yields an interpretable analytical expression for exhaust mass flow, facilitating interpretation and potential integration into control. The results indicate that deep learning enables accurate and interpretable prediction of key exhaust parameters in dual-fuel engines, supporting emission assessment and mitigation strategies relevant to underground mining operations. These findings support future integration with ventilation models and real-time monitoring frameworks. Full article

The floating oil seal (FOS) is a critical component in coal mining machinery, where frictional wear and high stress on the O-ring can lead to oil leakage and eventual FOS failure, significantly impairing equipment performance. To address this issue, this study proposes a novel ceramic-coated floating oil seal (NCCFOS) composite structure that enhances wear resistance without modifying the existing sealing cavity configuration. A two-dimensional axisymmetric finite element model of the NCCFOS was developed based on the Mooney–Rivlin constitutive model, considering the O-ring assembly process for improved accuracy. The model was analyzed under oil pressure loading, with parametric studies examining the influence of oil pressure, assembly clearance, and material hardness on O-ring stress, contact pressure, and frictional stress distribution in the floating seal ring. The results demonstrate that accounting for the assembly process yielded more realistic stress predictions compared to conventional modeling approaches. The NCCFOS design effectively mitigated stress concentrations, reduced O-ring wear, and extended fatigue life, offering a practical solution for enhancing the reliability of coal mining machinery seals. Full article

Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article

To address the limitations of traditional elasticity theory in analyzing the dynamic response of soft coal under external impact, this study establishes a vibration control equation with an analytical solution based on Hamiltonian mechanics. Key control parameters within the equation were solved to determine the theoretical dominant vibration modes and natural frequencies of the weakest coal layer. Triangular and rectangular waves were transformed via FFT to analyze their harmonic components, and the superposition of the first four harmonics was selected as the input impact signal. The modal and natural frequency changes during the fragmentation of the central weak zone under external impact were simulated, and the dynamic displacement response was analyzed. The results indicate a strong response frequency range of 4.4–5.2 Hz, with the rectangular wave identified as the most effective response waveform. A similarity simulation platform was constructed, and experimental data showed that the velocity and displacement response trend of the coal mass aligned closely with theoretical predictions. Therefore, in actual underground operations, emphasis should be placed on monitoring low-frequency vibrations in mines, minimizing rectangular wave disturbances in the low-frequency range, and implementing pressure relief measures in high-risk zones to reduce the likelihood of coal and gas outbursts. By separately modeling high-risk zones and analyzing their dynamic response under external impact, this study explains the outburst mechanism of the weakest layer in soft coal from a dynamic perspective. Combining theoretical and experimental approaches, it provides a new theoretical basis for understanding and preventing coal and gas outbursts. Full article

Background: Facing global wheat production pressures such as environmental degradation and reduced cultivated land, breeding innovation is urgent to boost yields. Genomic selection (GS) is a useful wheat breeding technology to make the breeding process more efficient, increasing the genetic gain per unit time and cost. Precise genomic estimated breeding value (GEBV) via genome-wide markers is usually hampered by high-dimensional genomic data. Methods: To address this, we propose GRE, a framework combining genome-wide association study (GWAS)’s biological significance and random forest (RF)’s prediction efficiency for an explainable machine learning GS model. First, GRE identifies significant SNPs affecting wheat yield traits by comparison of the constructed 24 SNP subsets (intersection/union) selected by leveraging GWAS and RF, to analyze the marker scale’s impact. Furthermore, GRE compares six GS algorithms (GBLUP and five machine learning models), evaluating performance via prediction accuracy (Pearson correlation coefficient, PCC) and error. Additionally, GRE leverages Shapley additive explanations (SHAP) explainable techniques to overcome traditional GS models’ “black box” limitation, enabling cross-scale quantitative analysis and revealing how significant SNPs affect yield traits. Results: Results show that XGBoost and ElasticNet perform best in the union (383 SNPs) of GWAS and RF’s TOP 200 SNPs, with high accuracy (PCC > 0.864) and stability (standard deviation, SD < 0.005), and the significant SNPs identified by XGBoost are precisely explained by their main and interaction effects on wheat yield by SHAP. Conclusions: This study provides tool support for intelligent breeding chip design, important trait gene mining, and GS technology field transformation, aiding global agricultural sustainable productivity. Full article

Water hammer is a critical transient phenomenon in pumping systems, occurring when a sudden change in flow velocity generates pressure waves propagating along the pipeline. This study focuses on the dynamic response of a long rising pipeline subjected to an emergency pump shutdown, with particular emphasis on the sudden release and propagation of hydraulic energy in the form of pressure waves. Such scenarios are typical for mine dewatering and water supply systems with high elevation differences. Two numerical approaches were investigated: the Method of Characteristics (MOC) implemented in TSNet as a reference model, and the Train Analogy Method (PKP) implemented in MATLAB R2024b/Simulink, where the fluid is represented as discrete masses connected by elastic links, enabling the inclusion of pump and motor dynamics. Simulations were performed for two configurations: first–with a check valve installed only at the pump discharge and second–with a check valve at the pump discharge and in the middle of the pipeline. The results demonstrate that both models capture the essential features of water hammer: a sharp initial pressure drop, the formation of transient waves, and pressure oscillations with decreasing amplitude. These oscillations reflect the propagation and gradual dissipation of hydraulic energy stored in the moving fluid, primarily due to frictional and elastic effects within the pipeline. The presence of a check valve accelerates the attenuation of oscillations, effectively reducing the impact of returning waves on the downstream pipeline. The novelty of this study lies in the use of the PKP method to simulate transient flow and energy exchange in long rising pipelines with dynamic pump behavior. The method offers a physically intuitive and modular approach that enables the modelling of local flow phenomena, pressure wave propagation, and system components such as pump–motor inertia and check valves. This makes PKP a valuable tool for investigating complex water hammer scenarios, as it enables the analysis of pressure wave propagation and damping, providing insight into the scale and evolution of energy released during sudden operational incidents, such as an emergency pump shutdown. The close agreement between the PKP and MOC results confirms that the PKP method implemented in Simulink is a reliable tool for predicting transient pressure behavior in hydraulic installations and supports its use for further validation and dynamic system analysis. Full article

The distribution characteristics of adsorbed CH₄ across pores of various sizes underpin coal mine gas disaster prevention, resource assessment, and efficient coalbed methane (CBM) extraction. Utilizing Grand Canonical Monte Carlo (GCMC) simulations as a theoretical framework, this study establishes a mathematical model linking microscopic pore structure to macroscopic CH₄ adsorption thermodynamics in coal. Results reveal that micropores (0.38–1.5 nm) dominate pore structures in coal. For micropores (0.419–1.466 nm), CH₄ adsorption follows the Dubinin-Astakhov (DA) equation. The adsorption parameters change significantly as pore diameter increases, indicating that micropore size distribution predominantly governs CH₄ adsorption in coal. For larger pores (1.619–4.040 nm), Langmuir equation analysis reveals no significant changes in CH₄ adsorption parameters with increasing pore size, suggesting that the CH₄ adsorption behavior in pore structures larger than 1.5 nm is relatively consistent and does not vary substantially with respect to pore size. The accuracy of the mathematical model improves with coal rank, reducing prediction errors from 35.371% to 11.044%. Decomposed CH₄ adsorption isotherms reveal that while CH₄ adsorption capacity increases with equilibrium pressure for all pores, smaller pores achieve saturation at lower pressures. The proportion of total adsorption attributed to smaller pores peaks before declining with further pressure increases. Full article

The rock strata traversed by frozen shafts in coal mines located in western regions are predominantly composed of weakly cemented, water-rich sandstones of the Cretaceous system. Investigating the rheological damage behavior of saturated sandstone under frozen conditions is essential for evaluating the safety and stability of these frozen shafts. To explore the damage evolution and creep characteristics of Cretaceous sandstone under the coupled influence of low temperature and in situ stress, a series of triaxial creep tests were conducted at a constant temperature of −10 °C, under varying confining pressures (0, 2, 4, and 6 MPa). Simultaneously, acoustic emission (AE) energy monitoring was employed to characterize the damage behavior of saturated frozen sandstone under stepwise loading conditions. Based on the experimental findings, a fractional-order creep constitutive model incorporating damage evolution was developed to capture the time-dependent deformation behavior. The sensitivity of model parameters to temperature and confining pressure was also analyzed. The main findings are as follows: (1) Creep deformation progressively increases with higher confining pressure, and nonlinear accelerated creep is observed during the final loading stage. (2) A fractional-order nonlinear creep model accounting for the coupled effects of low temperature, stress, and damage was successfully established based on the test data. (3) Model parameters were identified using the least squares fitting method across different temperature and pressure conditions. The predicted curves closely match the experimental results, validating the accuracy and applicability of the proposed model. These findings provide a theoretical foundation for understanding deformation mechanisms and ensuring the structural integrity of frozen shafts in Cretaceous sandstone formations of western coal mines. Full article

This study addresses gas drainage challenges in the Pingdingshan NO.10 mine JI_15-16 coal seam through coupled COMSOL-FLAC^3D numerical simulations. The research evaluates the effectiveness of a cross-measure borehole drainage system. It analyzes the failure mechanisms of the surrounding rock in both the machine roadway and floor roadway of the 24130 working face under the influence of boreholes. The results demonstrate that extended drainage duration progressively reduces both gas content and pressure within the borehole-affected zone of the coal seam while enhancing the effective permeability of the JI_15-16 coal stratum. The operational system extracted 1,527,357 m³ of methane, achieving a pre-drainage efficiency of 59.18% through cross-measure boreholes. The measured gas content aligns with simulated predictions, though field-recorded gas pressure registered slightly higher than modeled values. This validated drainage design complies with the Pingmei Group’s regulations for coal and gas outburst prevention. Critically, cross-measure boreholes alter stress distribution around both coal and floor roadways, promoting plastic zone expansion. Consequently, during the development of the 24130 working face’s machine roadway, intensified ground pressure monitoring is essential near borehole locations in the roof, floor, and rib strata. Supplementary support reinforcement should be implemented when required to prevent rib spalling and roof collapse incidents. Full article

Blast-induced airblast poses a significant environmental and operational issue for surface mining, affecting safety, regulatory adherence, and the well-being of surrounding communities. Despite advancements in machine learning methods for predicting airblast, present studies neglect essential geomechanical characteristics, specifically rock mass strength (RMS), which is vital for energy transmission and pressure-wave attenuation. This paper presents a capuchin search algorithm-optimized multilayer perceptron (CapSA-MLP) that incorporates RMS, hole depth (HD), maximum charge per delay (MCPD), monitoring distance (D), total explosive mass (TEM), and number of holes (NH). Blast datasets from a granite quarry were utilized to train and test the model in comparison to benchmark approaches, such as particle swarm optimized artificial neural network (PSO-ANN), multivariate regression analysis (MVRA), and the United States Bureau of Mines (USBM) equation. CapSA-MLP outperformed PSO-ANN (RMSE = 1.120, R² = 0.904 compared to RMSE = 1.284, R² = 0.846), whereas MVRA and USBM exhibited lower accuracy. Sensitivity analysis indicated RMS as the main input factor. This study is the first to use CapSA-MLP with RMS for airblast prediction. The findings illustrate the significance of metaheuristic optimization in developing adaptable, generalizable models for various rock types, thereby improving blast design and environmental management in mining activities. Full article