Search Results (68)

Mine water inrush is a significant environmental catastrophe during the coal mining process, and the timely discrimination of the source of water inrush is the key to ensuring safe production in coal mines. This work suggests a mine water inrush—belief rule base (MWI-BRB) source discrimination model to overcome the interpretability and performance issues with conventional models. MWI-BRB firstly automatically constructs the reference values of prerequisite attributes using the Sum of Squared Errors—K-means++ algorithm, which effectively combines expert knowledge and data-driven methods, and solves the limitation of the traditional belief rule base model relying on specialist knowledge. Secondly, the hierarchical incremental structure solves the rule explosion problem caused by complex features while using XGBoost to select features. Finally, in the inference process, the model adopts an evidential reasoning algorithm to realize transparent causal inference, guaranteeing the model’s interpretability and transparency. The Penalized Covariance Matrix Adaptation Evolution Strategy algorithm optimizes the model parameters to increase the discriminative accuracy of the model even more. Experimental results on a real coal mine dataset (a total of 67 samples from Hebei, China, covering four water inrush sources) demonstrate that the proposed MWI-BRB achieves 95.23% accuracy, 95.23% recall, and 95.36% F1-score under a 7:3 training–testing split with parameter tuning performed via leave-one-out cross-validation. The near-identical values across accuracy, recall, and F1-score reflect the balanced nature of the dataset and the robustness of the model across different evaluation metrics. Compared with baseline models, MWI-BRB’s accuracy and recall are 4.78% higher than BPNN and 9.52% higher than KNN, RF, and XGBoost; its F1-score is 4.85% higher than BPNN, 10.64% higher than KNN, 10.19% higher than RF, and 9.65% higher than XGBoost. Moreover, the model maintains high interpretability. In conclusion, the MWI-BRB model can realize efficient and accurate water inrush source discrimination in complex environments, which provides a feasible technical solution for the prevention and control of mine water damage. Full article

Surface subsidence induced by underground mining in mining areas significantly alters surface topography and hydrogeological conditions, forming depressions and fissures, thereby affecting regional runoff-ponding processes and groundwater infiltration patterns. Accurate assessment of infiltration volumes in subsidence zones under heavy rainfall is crucial for designing underground drainage systems and evaluating water-inrush risks in open-pit to underground transition mines. Taking the surface subsidence area of the Dahongshan Iron Mine as a case study, this paper proposes a rainfall infiltration calculation method based on the precise delineation of surface ponding-infiltration zones. By numerically simulating the subsidence range, the study divides the area into two distinct infiltration characteristic zones under different mining states: the caved zone and the water-conducting fracture zone. The rainfall infiltration volume under storm conditions was calculated separately for each zone. The results indicate that high-intensity mining-induced subsidence leads to a nonlinear surge in stormwater infiltration, primarily due to the significant expansion of the highly permeable caved zone. The core mechanism lies in the area expansion of the caved zone as a rapid infiltration channel, which dominates the overall infiltration capacity multiplication. These findings provide a scientific basis for the design of mine drainage systems and the prevention of water-inrush disasters. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

Roof water inrush accidents in coal mine driving roadways occur frequently in China, accounting for a high proportion of major coal mine water hazard accidents and causing serious losses. Aiming at the lack of research on the mechanism of roof water inrush in driving roadways and the difficulty of predicting water inrush accidents, this paper constructs a local damage criterion for coal–rock mass and a seepage–fracture coupling model based on peridynamics (PD) bond theory. It identifies three zones of water-conducting channels in roadway surrounding rock, the water fracture zone, the driving fracture zone, and the water-resisting zone, revealing that the damage degree of the water-resisting zone dominates the transformation mechanism between delayed and instantaneous water inrush. A discriminant function for the effectiveness of water-conducting channels is established, and a single-index prediction and evaluation system based on damage critical values is proposed. A “geometry damage” dual-control water inrush prediction model within the PD framework is constructed, along with a non-local action mechanism model and quantitative prediction method for water inrush. Case studies verify the threshold for delayed water inrush and criteria for instantaneous water inrush. The research results provide theoretical tools for roadway water exploration design and water hazard prevention and control. Full article

The Jurassic aquifer in Northwest China is the key aquifer for mine water filling, which is significant due to its loose structure, large porosity, strong rock permeability, and fracture development characteristics. In addition, the water richness in space is extremely uneven, and many coal mine roof water inrush events are closely related to it. A case of evaluation of water-richness of the roof sandstone in the 3-1 coal seam of the Bayangaole minefield was analyzed in depth, and the evaluation index system is established based on lithology and structural characteristics. Specifically, the evaluation indexes are under the influence of the influencing factors of lithology, the density of fault intersection endpoints, and the density of fault scale and the strength of folds as the influencing factors of structure. On this basis, the set pair analysis-variable fuzzy set coupling evaluation method is introduced to form a targeted water-rich evaluation model of a roof sandstone aquifer. By using the coupling method of set pair analysis and variable fuzzy set, a targeted evaluation model is formed to realize the organic integration of indicators. Through the comprehensive analysis of the relative zoning of water abundance and the data from the borehole pumping (drainage) test, the distribution of water abundance grade in the study area is clarified. Full article

In coal seam mining operations, the presence of overlying water bodies presents persistent challenges, particularly during multi-seam extraction, where water accumulation in upper seam goafs requires careful management. This study examined the Lingzhida Coal Mine, focusing on the geological conditions of the 3# seam (upper) and the 15# seam (lower), as well as the distribution of water accumulation in the corresponding goafs. The mechanism of water inrush from the upper goaf was studied, and the role of the water-resisting belt (WRB) is suggested. By utilizing empirical equations and field measurements, a method for calculating the floor fracture depth of the 3# seam and the roof fracture height of the 15# seam was derived through multi-linear regression analysis. Based on the relationship between the thickness of the WRB (H_w) and the protective layer (H_p), a classification criterion for the water-inrush risk (the likelihood of water entering the lower seam from the upper goaf) is proposed. The mining area was divided into four risk zones: high-risk (H_w < 0), medium-risk (0 ≤ H_w < 0.5H_p), low-risk (0.5H_p ≤ H_w < H_p), and safe (H_w ≥ H_p). Then, an adaptive zoning approach for water-preserved mining was introduced, considering the spatial distribution of goaf water. This approach incorporates water-preserved mining technologies, including the staggered layout of working faces, reduction in mining height, and the transfer–storage of water resources. These research findings provide crucial insights for ensuring the safe and efficient extraction of the multi-seam. Full article

Subsea tunnels cross complex geological structures, such as weathered troughs with fractured rock masses and high permeability, and are prone to water and mud inrush. To minimize the risk of subsea tunnelling, a novel method consisting of a multi-index evaluation system and a computational model using the interval number TOPSIS method was established. The multi-index evaluation system was formed by eight evaluation indices that can potentially affect water and mud inrush: sea depth, subsea tunnel burial depth, scale of weathering trough, interface angle, strength of surrounding rock, permeability of weathering trough, cyclical footage, and grouting reinforced region. The risk levels of water and mud inrush were divided into four grades. Considering the uncertainty of the evaluation indices, an evaluation vector of interval numbers was adopted. The triangular fuzzy number membership function was used to determine the membership degree, and the 1–9 scales method was used to construct the judgment matrices, which can obtain the weight of evaluation indices. Furthermore, the weight values of the evaluation indices combined with the membership degree were used to obtain the result vector, which can be analyzed using the interval number TOPSIS method. This novel assessment method was applied to the FWK15+350 of the Haicang tunnel successfully. The risk level fell into IV, which represents a high-risk section. The results showed a high degree of congruence with the prevailing circumstances, thereby validating the credibility of the proposed methodology. Full article

To address the challenge of dynamic monitoring during grouting operations in coal mine fault zones under pressurized mining, this study proposes the Borehole–Tunnel Joint Resistivity Method (BTJRM). By integrating three-dimensional (3D) electrode arrays in both tunnels and boreholes with 3D resistivity inversion technology, this approach enables fully automated underground data acquisition and real-time processing, facilitating comprehensive dynamic monitoring of grout propagation. A case study was conducted on a coal mine fault grouting project, where tunnel and borehole survey lines were deployed to construct a 3D cross-monitoring network, overcoming the limitations of traditional 2D data acquisition. Finite volume method and quasi-Gauss–Newton inversion algorithms were employed to analyze dynamic resistivity variations, enhancing spatial resolution for detailed characterization of grout migration. Key findings include: (1) Grout diffusion reduced resistivity by 10%, aligning with electrical response patterns during fracture-filling stages; (2) 3D inversion reveals that grout propagates along the principal stress axis, forming a “Y”-shaped low-resistivity anomaly zone that penetrates the fault structural block and extends into roadway areas. The maximum planar and vertical displacements of grout reach 100 m and 40 m, respectively. Thirty days post-grouting, resistivity recovers by up to 22%, reflecting the electrical signature of grout consolidation; (3) This method enables 3D reconstruction of grout diffusion pathways, extends the time window for early warning of water-conducting channel development, and enhances pre-warning capabilities for grout migration. It provides a robust framework for real-time sealing control of fault strata, offering a novel dynamic monitoring technology for mine water inrush prevention. The technology can provide reliable grouting evaluation for mine disaster control engineering. Full article

The accuracy of coal mine water inrush prediction models is affected mainly by the small number of samples and difficulty in feature extraction. A new data augmentation water inrush prediction method is proposed. This method uses the natural neighbor theory and mutual information sparse autoencoder-improved SMOTE to augment and predict the risk of water inrush. By learning features through the autoencoder, we can achieve better separation between classes and weaken the influence of data overlap between classes in the original sample. Then, the natural neighbor search algorithm is used to determine the intrinsic neighbor relationships between samples, remove outliers and noise samples, and use different oversampling methods for borderline samples and center samples in the minority class. Synthetic samples are generated in the feature space, mapped back to the original space, and merged with the original samples to form an expanded water inrush dataset. Finally, the experiment demonstrates that the enhanced SMOTE oversampling algorithm suggested in this paper broadens the dataset. With a Gmean value of 0.9025 from training with the standard dataset, it outperforms the contrast algorithm, SMOTE average of 0.8581, B-SMOTE average of 0.873, and ADASYN average of 0.8909. Additionally, it performs well in the coal mine floor water inrush dataset, increasing the water inrush prediction algorithm’s accuracy. Full article

Adjacent, multi-working face mining can expand the range of disturbed overburden, increasing the risk of triggering roof water inrush, which threatens the safe operation of coal mines. In this paper, we propose a risk identification method for roof water inrush under multi-working face mining conditions based on the theory of Key Strata and Full Mining Disturbance. Firstly, the key strata of the overburden are determined based on lithological and structural data from exploration boreholes. A formula is then derived to calculate the critical dimension (L) of the working face that could induce a fracture in the key stratum. The relationship between L and the combined width of the preceding and adjacent working faces is analyzed to assess whether the key stratum is fractured and how it affects the preceding working face. Finally, the height of the water-conducting fracture zone is predicted. The impact of repeated disturbances from multi-working face mining is evaluated to determine whether the height of the water-conducting fracture zone in the preceding working face increases, thereby enabling risk identification for roof water inrush under multi-working face mining conditions. Taking the multi-working faces of the Banji Coal Mine in Anhui Province as a case study, the predicted height of the water-conducting fracture zone is 60 m, with no risk of delayed roof water inrush in the preceding working face. Both numerical simulation results and field measurements of the development height of the water-conducting fracture zone confirm the effectiveness of this method. It is capable of accurately predicting the development height of the water-conducting fracture zone under multi-working face mining conditions and identifying the associated risk of roof water inrush, thus providing a valuable reference for ensuring safe mining operations in multi-working face mining conditions. Full article

Bed-separation water hazards are a common and very harmful mining disaster in the mining areas of western China in recent years, which seriously threatens the safe mining of rich and thick coal seam resources in the West. The Yonglong mining area has become a high-risk area for bed-separation water hazards due to its particularly thick coal seams and strong water-rich overlying strata. In view of this, this paper investigates the development height of a water-flowing fractured zone in the fully mechanized caving mining of an ultra-thick coal seam in the Yonglong mining area, the evolution law of the bed separation of overlying strata, and the process of water inrush from a bed separation. Based on the measured water-flowing fractured zone height data of the Yonglong mining area and several surrounding mines, a water-flowing fractured zone height prediction formula suitable for the geological conditions of the Yonglong mining area was fitted. By using discrete element numerical simulation and laboratory similarity simulation, the evolution law of overlying strata separation under the conditions of fully mechanized caving mining in the study area was analyzed, and the space was summarized into “four zones, three arches, and five zones”. Through the stress-seepage coupling simulation of the water inrush process of the roof separation in the fully mechanized caving mining of an ultra-thick coal seam, the migration, accumulation, and sudden inrush of water in the aquifer in overlying strata under the influence of mining were analyzed, and the variation in the pore water pressure in the process of water inrush during coal seam mining separation was summarized. The pore water pressure in the overlying strata showed a trend of first decreasing, then increasing, and, finally, stabilizing. Combined with the height, water inrush volume, and water-rich zoning characteristics of the water-flowing fractured zone of the 1012007 working face of the Yuanzigou Coal Mine, the danger of water inrush from the overlying strata separation of the working face was evaluated. It is believed that it has the conditions for the formation of water accumulation and separation, and the risk of water inrush is high. Prevention and control measures need to be taken on site to ensure mining safety. The research results have important guiding significance for the assessment and prevention of water inrush hazards in overlying strata during fully mechanized longwall top-coal caving of ultra-thick coal seams with similar geological conditions worldwide. Full article

In this paper, we investigate the evolution characteristics of floor failure during pressured mining in extra-thick coal seams. A mechanical expression relating floor failure depth to seam thickness is established based on soil mechanics and mine pressure theory. The findings reveal a linear relationship between seam thickness and floor failure depth; specifically, as the coal seam thickens, the depth of floor failure increases. To simulate the mining process of extra-thick coal seams, FLAC3D numerical simulation software is utilized. We analyze the failure process, failure depth, and the behavior of water barriers at the coal seam floor under the influence of extra-thick coal seam mining from three perspectives: rock displacement evolution in the floor, stress evolution in the floor, and plastic deformation. Based on geological characteristics observed in the Longwanggou mine field, we establish a main control index system for assessing floor water-inrush risk. This system comprises 11 primary control factors: water abundance, permeability, water pressure, complexity of geological structure, structural intersection points, thickness of both actual and equivalent water barriers, thickness ratio of brittle–plastic rocks to coal seams, as well as depths related to both coal seams and instances of floor failure. Furthermore, drawing upon grey system theory and fuzzy mathematics within uncertainty mathematics frameworks leads us to propose an innovative approach—the interval grey optimal clustering model—designed specifically for risk assessment concerning potential floor water inrush during pressured mining operations involving extra-thick coal seams. This method of mine water inrush risk assessment is applicable for popularization and implementation in mines with analogous conditions, and it holds practical significance for the prevention of mine water damage. Full article

Numerous floor water inrush (FWI) disasters have occurred during the roof weighting period in China. Therefore, to clarify why FWI accidents tend to cluster at a specific mining stage, a novel method for evaluating the failure depth of mining floors (FDMF) under dynamic loads induced by roof breakage is proposed in this study. By employing Matlab programming, the stress distribution and failure patterns of the intact floor were analyzed, revealing the dynamic stress response and failure characteristics. In addition, the accuracy of the proposed theoretical model was further verified through numerical simulation and field measurement. The results indicate that dynamic loads significantly impact vertical stress and shear stress, but only have a minor impact on horizontal stress. This leads to an expansion of the stress concentration zone and an increase in the intensity of the mining floor. Moreover, the FDMF is notably enhanced under the dynamic load induced by roof weighting. Besides, both the numerical simulations and field measurement results align closely with the theoretical predictions, which confirm the effectiveness of the proposed method. This study provides a theoretical foundation for understanding FWI mechanisms under the combined influence of dynamic and static loads. Full article

Existing aquifer water richness evaluation methods typically employ fixed indicator weights, failing to account for variations within individual controlling factors or interactions among multiple factors. This study introduces an enhanced water richness index method based on zoned variable weighting theory. Through unified normalization of water inrush controlling factors for each main mining coal seam, construction of variable weighting status vectors, division of unified variable weighting intervals, and determination of vulnerability index zoning thresholds, the method dynamically assigns weights to different evaluation indicators and adjusts weights based on varying state values. The study proposes a standardization and dimensionless processing approach for key controlling factors influencing aquifer water richness, including lithological differences, hydraulic properties, and weathering degrees. Using K-means clustering, variable-weighting interval thresholds are established for each controlling factor. The research also explores the construction of state variable-weighting vectors and the determination of adjustment parameters, quantitatively assessing the interactive relationships and relative importance of controlling factors on aquifer water richness. A variable-weighting-based water richness index model is developed. Taking the weathered bedrock aquifer of the No. 2 coal seam roof in the Hongliulin coal mine as a case study, this paper demonstrates the specific implementation steps of the proposed method. The results show that the variable-weighting model more accurately reflects aquifer heterogeneity and offers higher predictive accuracy compared to traditional constant-weighting methods. Full article

As coal seams are mined at greater depths, the threat of high water pressure from the confined aquifer in the floor that mining operations face has become increasingly prominent. Taking the Madaotou mine field in the Datong Coalfield as the research object, in the context of mining under pressure, for the main coal seams in the mining area, first of all, an improved evaluation method for the vulnerability of floor water inrush is adopted for hazard prediction. Secondly, numerical simulation is used to conduct a simulation analysis on the fault zones in high-risk areas. By using the fuzzy C-means clustering method (FCCM) to improve the classification method for the normalized indicators in the original variable-weight vulnerability evaluation, the risk zoning for water inrush from the coal seam floor is determined. Then, through the numerical simulation method, a simulation analysis is carried out on high-risk areas to simulate the disturbance changes of different mining methods on the fault zones so as to put forward reasonable mining methods. The results show that the classification of the variable-weight intervals of water inrush from the coal seam floor is more suitable to be classified by using fuzzy clustering, thus improving the prediction accuracy. Based on the time effect of the delayed water inrush of faults, different mining methods determine the duration of the disturbance on the fault zones. Therefore, by reducing the disturbance time on the fault zones, the risk of karst water inrush from the floor of the fault zones can be reduced. Through prediction evaluation and simulation analysis, the evaluation of the risk of water inrush in coal mines has been greatly improved, which is of great significance for ensuring the safe and efficient mining of mines. Full article