Search Results (209)

Freeze–thaw cycles cause mechanical deterioration and instability of slope rock masses in open-pit coal mines located in the cold regions of Northwest China. In this study, the research object is fine-grained sandstone from the Yan’an Formation in the Tarangole mining area of the Ordos Basin. Here, indoor freeze–thaw cycling, uniaxial compression, and triaxial compression tests were conducted to systematically analyze the deformation behavior, strength evolution, and failure modes of the sandstone under varying numbers of freeze–thaw cycles, freezing temperatures, and confining pressures, thereby revealing its freeze–thaw damage mechanism. The results show that the number of freeze–thaw cycles is the dominant factor affecting the elastic modulus. Freezing temperatures (especially between −5 °C and −15 °C) and the number of freeze–thaw cycles (particularly the first 10 cycles) significantly reduce peak strength. In addition, confining pressure can significantly enhance the resistance to deformation (under 15 freeze–thaw cycles, the elastic modulus increases by 181.8% as confining pressure rises from 0 to 2 MPa). Within the low confining pressure range (0–1.5 MPa), peak strain decreases monotonically with increasing confining pressure and is independent of the number of freeze–thaw cycles. Finally, the increase in the number of freeze–thaw cycles and the decrease in temperature jointly promote crack development, and the failure mode shifts from pure shear to a shear-tension composite mode. The underlying cause lies in the evolution of interparticle cementation within the soil skeleton and in the associated pore–crack structure. In addition, based on fracture damage mechanics and the modified Weibull distribution, a damage evolution equation and a constitutive model for sandstone considering freeze–thaw cycles and temperature effects were established and validated. Therefore, the research findings can provide a theoretical basis for slope support, freeze–thaw disaster prevention and mitigation, and stability assessment in the Tarangole mining area and other cold regions. Full article

Dust and coal mine gas in deep mines are highly prone to causing fires, and the cyclic high temperatures generated by such fires are one of the key factors contributing to the instability of deep rock structures. To research the dynamic splitting tensile mechanical properties of sandstone subjected to high-temperature cycling, impact splitting tensile tests were performed on sandstone specimens under normal temperature and after high-temperature cycling treatments ranging from 250 °C to 900 °C using a split Hopkinson pressure bar (SHPB) with increasing cyclic temperature. The average dynamic tensile strength of sandstone specimens declines following a quadratic function, dropping from 18.07 MPa at T = 150 °C to a minimum value of 3.08 MPa, representing a maximum reduction of 82.96%. The dynamic strain and average strain rate exhibit increasing trends following exponential and logarithmic functions, respectively, while the dynamic elastic modulus exhibits a logarithmic declining trend. As the cyclic temperature grows, the degree of fragmentation of the specimens intensifies, transitioning from axial splitting failure to pulverization failure, with fragment size decreasing and fractal dimension exhibiting increasing trends. For temperatures between 450 °C and 600 °C, the dynamic tensile strength, dynamic strain, average strain rate, dynamic elastic modulus, average particle size, and fractal dimension all show a distinct interval behavior. As the cyclic temperature rises, the incident, reflected, and transmitted energies gradually decline. A higher fragmentation energy density corresponds to more severe specimen fragmentation, and the average fragment size follows a negative quadratic relationship with fragmentation energy density, which effectively quantifies the dynamic splitting tensile fragmentation behavior of rock. The findings of this study regarding the dynamic behavior and damage evolution of sandstone under cyclic high-temperature conditions can serve as a reference for assessing rock mass stability in high-temperature applications such as underground engineering and resource development. Full article

Acidic mine water generated during underground CO₂ sequestration and sulfide oxidation can alter the pore-fracture structure of coal, and threaten the stability of abandoned mine spaces. However, the mechanism through which acidic environments influence the deterioration of coal remains insufficiently understood. In this study, uniaxial compression experiments were conducted on coal samples treated with solutions with different pH values, and acoustic emission (AE) monitoring technology was used to characterize fracture activity and damage evolution during loading. A quantitative model linking acidity to the mechanical behavior of coal was established by integrating fractal theory with damage mechanics. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) were further employed to reveal the microstructural and mineralogical mechanisms of coal deterioration. The results show that acidic environments significantly degrade the mechanical properties of coal samples. With decreasing pH, peak stress and elastic modulus of the selected representative sample progressively decrease, and the failure mode becomes increasingly fragmented and dispersed. At pH = 1, the degradation of peak stress and elastic modulus reaches 73.01% and 49.38%, respectively. Increasing acidity also enhances AE activity during loading and increases the correlation dimension, indicating greater crack complexity and instability. On this basis, the proposed quantitative model accurately describes the transformation process of coal samples from microscopic damage to macroscopic mechanical degradation induced by acidity. SEM and XRD results further show that stronger acidity promotes pore enlargement, crack interconnection, mineral dissolution, secondary mineral formation, and weakening of cementation, revealing the physical essence of the multi-scale damage and degradation of coal samples. The findings can provide a theoretical basis for assessing coal stability in acidic environments and ensuring the safe storage of CO₂ in abandoned mines. Full article

The rapid growth of battery energy storage and electric vehicles has increased lithium demand and intensified the attention given to the environmental performance of alternative extraction pathways. Conventional life cycle assessments (LCA) of lithium production typically report midpoint indicators in physical units, which limits cross-category comparison and reduces their usefulness for economic and policy analysis. This study presents a comparative monetized LCA of lithium carbonate equivalent (LCE) production from three pathways: solar brine evaporation, hard-rock spodumene mining, and geothermal brine recovery. Using the TRACI 2.1 midpoint results reported in a prior LCA, six impact categories—global warming, smog formation, acidification, respiratory effects, carcinogenic toxicity, and non-carcinogenic toxicity—are converted into monetary values through a benefit-transfer, damage-cost approach. Total environmental external costs are estimated at USD 11.85/kg LCE for solar brine evaporation, USD 9.45/kg LCE for spodumene mining, and USD 4.11/kg LCE for geothermal brine recovery (all USD amounts are expressed in $2025 unless otherwise mentioned). Smog formation contributes more than 80% of the total monetized damages across all pathways, while toxicity-related impacts account for a smaller share than implied by the normalized midpoint results. Monetization changes the relative ranking of the solar brine and spodumene pathways, while indicating that geothermal brine recovery has the lowest monetized external cost among the impact categories evaluated. These findings show that monetized LCA can complement conventional midpoint assessment and provide more decision-relevant insights for policy and economic evaluation. Full article

Non-coal mines constitute a fundamental pillar of the global energy transition, providing essential raw materials across various sectors. Accidents in these facilities cause severe direct damage, including personal injuries and economic losses, while triggering broader systemic impacts, such as supply chain disruptions of essential energy materials. Taking China as an example, roof fall and rib spalling accidents account for 30% to 40% of all safety incidents in non-coal mines. Consequently, investigating the occurrence and evolutionary process of roof fall and rib spalling risks in these environments holds significant importance. To identify the fundamental factors of the accident, the 24Model and the fault tree are employed in this research. Expert elicitation and fuzzy set theory are used to determine the occurrence probability of each basic event. To overcome the limitations of traditional static risk assessment, a dynamic Bayesian network is introduced to capture the time-varying characteristics of risk factors, evaluating the overall accident probability over a 52-week period, equivalent to approximately one year. In this study, 30 basic events and 18 intermediate events were identified. Quantitative results show that effectively controlling the critical events (X26, X4, X20, X27, X2) reduces the accident probability by 56.39%. Furthermore, the dynamic Bayesian network analysis demonstrates that under the specific assumption of continuous risk accumulation across five dimensions (human, material, management, cultural, and environmental factors) without the prompt implementation of targeted interventions, the occurrence probability of the accident reaches 0.95023 after 52 weeks. The results demonstrate that this model surpasses static models by effectively identifying the critical causal factors of the accident and evaluating their occurrence probabilities through systematic causation analysis and dynamic accident evolution. This approach facilitates precise accident early warning, offering a practical reference for relevant enterprises and personnel involved in roof safety management. Full article

Blasting-induced ground vibrations, typically quantified by peak particle velocity (PPV), pose one of the most critical environmental challenges in surface mining and can damage nearby structures and disrupt surrounding ecosystems. Consequently, the development of reliable and accurate predictive models is essential for designing safe, environmentally responsible, and sustainable blasting operations. This study develops a robust predictive framework using a harmonized database of 506 blasting events, from which 386 high-quality records were retained after preprocessing to model PPV as a function of charge per delay (Q), monitoring distance (R), and rock mass rating (RMR). Several machine learning (ML) algorithms, including artificial neural networks trained using the Levenberg–Marquardt algorithm (ANN-LM), adaptive neuro-fuzzy inference systems (ANFIS), Gaussian process regression (GPR), and decision trees (DT), were evaluated alongside conventional empirical models such as the USBM, Ambraseys–Hendron, Langefors–Kihlstrom, and BIS. To further enhance predictive capability, two optimization strategies, Bayesian optimization (BO) and differential evolution (DE), were applied to the GPR model, producing optimized BO-GPR and DE-GPR variants. Model performance was assessed using the correlation coefficient (r), variance accounted for (VAF), mean absolute error (MAE), and relative root mean square error (RRMSE). Results indicate that the BO-GPR model achieved the best predictive performance during testing for both the two-input (Q, R) and three-input (Q, R, RMR) configurations, with r values of 0.97426 and 0.98381, respectively, and VAF values exceeding 94%. SHAP analysis revealed monitoring distance as the dominant attenuating factor controlling PPV. The optimized framework provides an accurate, interpretable tool for vibration prediction and precision blast design, supporting environmentally responsible, sustainable mining operations. Full article

Abandoned mine methane (AMM) emissions represent a significant public safety and environmental hazard in post-industrial urban areas. Uncontrolled subsurface gas migration may lead to explosive accumulations in confined spaces, human exposure, infrastructure damage, and additional greenhouse gas emissions. This study develops an integrated risk assessment framework for AMM in urban environments, combining quantitative analysis of field monitoring data with semi-quantitative probability–consequence risk matrices and multi-factor evaluation. Methane concentrations were measured at 41 monitoring points during three campaigns (August–September 2024). A total of 42 influencing factors were identified and classified into seven categories (geological, mining, hydrogeological, meteorological, anthropogenic, biological, and special phenomena). Exceedance probabilities of critical thresholds were estimated with 95% confidence intervals. Consequence weights were derived using the Analytic Hierarchy Process (AHP) based on a five-expert panel (CR = 0.043). The framework was applied to the urban area of Lupeni, Romania, where methane concentrations of up to 54% vol. were measured during borehole screening measurements (subsurface probe points). Elevated concentrations were detected four days after commissioning of a new gas pipeline. Gas chromatographic analysis excluded pipeline leakage and confirmed a mining-related source. Results indicate a localized critical risk (R = 25 on a 1–25 scale) in hotspot P2, with a 95% confidence interval for the probability of exceeding the 3% vol. alert threshold of [0.885–1.00], justifying immediate mitigation through controlled drainage. Post-intervention monitoring showed a reduction to instrumentally undetectable levels by February 2025. The study demonstrates that administrative mine closure does not eliminate residual methane risk and proposes a complete decision-support algorithm (URBAN-MINE-RISK) for similar urban settings. The applicability of structural reliability methods (e.g., FORM) is discussed as a future research direction. The methodology is transferable to other post-mining regions in Central and Eastern Europe. Full article

Coal mining beneath rivers in thick collapsible loess areas involves prominent risks of surface subsidence, riverbed damage, and water inrush, which threaten both mining safety and land–water ecological stability. Taking the Dan River Coal Mine in Shanxi Province, China, as a case area, this study establishes a systematic safety assessment and adaptive remediation framework for longwall mining under complex geological conditions involving collapse columns, dynamic river hydrology, and collapsible loess. A multi-method analytical approach integrating theoretical calculation, 3DEC numerical simulation, and engineering analogy is used to determine the development height of water-conducting fracture zones and the stability of collapse columns. On this basis, a 55 m wide waterproof coal–rock pillar is designed, and the secondary open-off cut is optimized. Surface deformation monitoring shows a maximum surface subsidence of 3.9 m and reveals key movement angles specific to thick collapsible strata. These results support the formulation of adaptive mining control strategies and integrated river protection measures, including composite geomembrane anti-seepage, gabion reinforcement, and overburden grouting for subsidence mitigation. The integrated technical system of pre-mining evaluation, dynamic process control, and post-mining remediation effectively protects river integrity, controls land deformation, and reduces environmental impacts. This study provides a replicable model for safe coal resource extraction, subsidence management, and land–water environmental protection in similar mining areas under rivers and thick collapsible loess conditions. Full article

Ultra-large underground metal mines often have complex surrounding rock structures, making traditional assessment methods inadequate for warning against the sudden failure of highly brittle rock masses. To accurately identify high-risk rock layers, this study combines Brazilian splitting tests with acoustic emission (AE) monitoring on four typical surrounding rocks. A normalized damage–stress brittleness coefficient (NDBC) is proposed, and Gaussian mixture model (GMM) clustering is employed to analyze crack evolution mechanisms. Different from conventional brittleness indexes merely based on mechanical parameters, the proposed NDBC characterizes rock brittleness from the perspective of progressive damage evolution driven by acoustic emission microfracture information, providing a dynamic evaluation basis for sudden instability in highly brittle rock masses. The GMM clustering automatically identifies crack features and accurately quantifies the transition from tensile peak to increasing shear during the failure process. The research shows that: (1) AE characteristics during the failure stage are manifested as medium- to high-frequency signals caused by small-scale cracks. (2) Siliceous limestone exhibits extremely high brittleness (NDBC of 0.07) and sudden failure due to the difficulty of microcrack propagation, posing a greater risk of instability and potential overall collapse during mining; in contrast, granite (NDBC of 0.23) is more ductile, showing progressive damage accumulation. (3) Initial rock splitting failure is primarily tensile cracking, with shear cracking increasing as failure approaches, transitioning the failure mechanism to a tensile–shear composite mode. Therefore, establishing a differentiated monitoring and prevention system based on AE main frequency identification and GMM analysis, designating siliceous limestone surrounding rock areas as key prevention zones, can effectively reduce the risk of sudden instability and ensure safe mining operations. Full article

Sand extraction drastically alters ecosystem structure and initiates conditions for primary succession development. Forest stands aged 9, 16, 19, and 28 years were surveyed to assess understory vegetation and epiphytic lichen communities in post-mining sand and gravel quarries in eastern Latvia. Community structure and functional traits were analyzed. Younger stands (9–19 years) exhibited the highest understory species diversity, dominated by hemicryptophytes, open-habitat grasses, and low-to-moderate ecological value lichens, while older stands (28 years) supported high-value epiphytic lichens and understory species typical of oligotrophic boreal forests. In 9-year-old stands, high-value epiphytic lichens comprised, on average, 5.7% (SE = 1.6) total lichen cover, while in 28-year-old stands it was 24.8% (SE = 1.9). Species with animal-mediated seed dispersal were more prevalent in younger stands, reflecting indications of animal presence based on vegetation composition and observed animal damage on trees. No invasive species were recorded, likely due to quarry isolation (≥1 km closest edge of the forest ecosystem) and proximity to mature forest margins. Our results highlight the multidimensionality of biodiversity by integrating two taxonomic groups and indicate high potential for passive natural regeneration toward Western Taiga 9010 habitat conditions under an oligotrophic environment. 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

The damage status of shield machine disc cutters directly impacts the safety and efficiency of tunnelling projects. Current manual inspection methods involve high risks and low efficiency, while existing detection methods suffer from low accuracy and poor real-time performance in complex environments, often lacking quantitative analysis capabilities. To address these issues, this paper proposes an intelligent identification and quantitative assessment method for disc cutter damage based on the Deep Forest (DF) model. First, an eddy current sensor calibration platform was established, and a mapping relationship between output voltage and actual wear was developed through piecewise fitting to achieve precise wear quantification. In the data preprocessing stage, signal quality was improved via filtering, and typical damage features such as edge chipping, cracks, and eccentric wear were extracted using pulse edge detection. These feature segments were then resampled to construct the model training dataset. The DF model utilizes a hierarchical ensemble structure to mine data correlations, enabling accurate identification of four states: normal, edge chipping, eccentric wear, and cracks. Simultaneously, a DF regression model was employed to provide continuous quantitative predictions of damage size. Experimental results show that the classification model achieved accuracies of 98%, 96%, and 96% on the training, validation, and test sets, respectively, with weighted average F1-scores exceeding 0.96. The regression model achieved a coefficient of determination (R²) of 0.9940 and a root mean square error (RMSE) of 0.4051 on the test set. Both models demonstrate excellent performance and generalization, achieving full coverage from “qualitative state identification” to “quantitative wear assessment,” thereby providing reliable decision support for cutter maintenance and replacement. Full article

Mineral resource extraction and urban expansion in resource-based cities have progressively degraded regional ecosystems, leading to increasingly fragmented ecological patterns. Ecological network resilience plays a critical role in maintaining regional ecological stability. In this study, we integrated landscape ecology and systems science to develop a network model and assess the resilience of ecological networks in the coal mining subsidence area with high groundwater levels. This study employed morphological spatial pattern analysis (MSPA) and circuit theory to construct the ecological network. A cascading failure model was further applied to simulate network dynamics under three attack strategies. Based on a comparative analysis of these strategies, we introduce the concept of “dangerous nodes” to identify priority conservation areas. The research results show that 101 ecological source areas and 255 ecological corridors were identified in the study area. Topologically, its ecological network is characterized by a small number of core nodes and a large number of secondary nodes. When the adjustable parameter is $\alpha <1.2$, targeting low-degree nodes may inflict more severe damage on the network. When $\alpha >1.2$, attacks against nodes with a high-degree or high betweenness centrality may have significant cascading failure implications. Our results show that the network’s critical threshold $T_c$ depends on the number of dangerous nodes in the attack set. The distribution of these nodes differs substantially between low-degree attacks and those targeting high-degree or high betweenness centrality nodes. These findings advance ecological network optimization and provide practical guidance for ecosystem conservation and restoration in resource-based cities. Full article

Leucoptera malifoliella has become a severe leaf-mining pest in Chinese apple orchards, especially under expanding organic and green cultivation practices, with effective management hindered by insufficient contemporary ecological data. To fill this gap, this 2023–2025 study conducted in Shijiazhuang, Hebei, combined field monitoring, morphological analysis, flight mill assays, and parasitoid release trials to clarify the moth’s phenology, develop rapid pupal sexing methods, quantify adult flight capacity, and assess Trichogramma dendrolimi biocontrol potential. The results showed five annual generations (overwintering as pupae), peak damage in July–August, and marked generational overlap. A reliable pupal sexing method was established via genital opening morphology. Adult flight peaked at 3 days post-emergence (max distance: 1.223 km), with no sexual dimorphism. Timely T. dendrolimi releases boosted parasitism rates, achieving 23.4–49.6% control efficacy during peak damage, with the parasitism rate positively correlated with efficacy. This study confirms the moth’s potential for generational increase under climate warming and medium-distance dispersal capacity, validating Trichogramma’s utility and laying a scientific foundation for precise, regionally coordinated ecological management. Full article

Background: Interindividual radiosensitivity is largely driven by genetic regulation of DNA damage recognition, repair, and cell-cycle control. TP53 and CDKN1A (p21) are key genomic markers associated with differential responses to ionizing radiation. Methods: This study investigated eight functional SNP markers across several key genes involved in DNA damage responses and cellular stress regulation, including TP53, CDKN1A/p21, APC, VEGF, XPD, and RAD51, in occupational groups exposed to chronic low-dose ionizing radiation at the Stepnogorsk Mining Chemical Combine and the Balkashinskoye uranium deposit. Genotyping was performed using PCR-based assays followed by restriction fragment length polymorphism (RFLP) analysis. Allele and genotype frequencies were compared between radiation-exposed workers and matched controls within Kazakh and Russian ethnic subgroups. Statistical differences were assessed using χ² tests, and associations with radioresistance were interpreted based on established functional characteristics of each polymorphism. Results: Four SNPs showed significant allele and genotype frequency shifts in radiation-exposed populations. The TP53 intron 3 insertion allele, TP53 intron 6 A allele, TP53 Pro72 (C) allele, and p21 codon 31 A allele were consistently enriched among exposed individuals. The strongest deviations were observed in Russian workers from Stepnogorsk (p < 0.01). These alleles are functionally associated with enhanced DNA repair efficiency, modified apoptotic responses, and strengthened cell-cycle checkpoint regulation. Conclusions: Significant enrichment of radioresistance-associated TP53 and CDKN1A (p21) variants was observed in uranium industry workers chronically exposed to low-to-moderate ionizing radiation. The observed patterns support a polygenic model of adaptive responses and emphasize the importance of genetic background in determining individual radiosensitivity under occupational exposure conditions. Full article