Accurate prediction of the water-conducting fracture zone height is essential for water inrush prevention and safe production in coal mining. Based on extensive in-situ measurements collected from longwall panels in different mining districts, five key indicators-mining thickness, mining depth, coal seam dip angle, panel length along dip, and the hard-rock lithology ratio coefficient-were analysed. Using regression analysis, the empirical formula for predicting the water-conducting fracture zone height was refined and a multivariate nonlinear regression model was fitted. An optimal BP neural network model with the Levenberg-Marquardt algorithm and a 5:8:4:1 topology was validated, and subsequently an LWMA-PSO-BP neural network model was developed by jointly introducing the mutation operator from genetic algorithms and a linearly decreasing inertia weight (LDIW) strategy. Model fitting accuracy and generalisation were evaluated; the results indicate that the LWMA-PSO-BP model achieved the best overall performance, with a mean absolute error of 2.40 m and a mean absolute percentage error of 4.27%. In the Hebi mining district, a joint geophysical investigation integrating a microtremor survey, borehole coring, and drilling fluid loss measurements was conducted, and the water-conducting fracture zone heights for Panels 2301, 2302, 2303, and 2304 at Hemei No. 5 Mine were determined as 129.05 m, 134.21 m, 141.50 m, and 138.20 m, respectively. Field validation shows that the relative errors of the multivariate nonlinear regression and BP neural network models were 5.52% and 4.85%, respectively, whereas the LWMA-PSO-BP model yielded a relative error of only 2.99%. These results provide a reference for predicting the water-conducting fracture zone height under varied coal mining conditions.
The height of a water-conducting fractured zone (WCFZ) is directly related to the design of water-preserved coal mining and water-hazard risk assessment in ecologically fragile mining areas in western China. Existing empirical formulas have limited regional adaptability, and individual machine learning models may show insufficient stability under small-sample and nonlinear data conditions. To address this issue, a heterogeneous Stacking ensemble prediction framework was constructed based on measured data from the Yushen mining area. Mining thickness, working face length, mining method, burial depth, coal seam dip angle, and hard strata proportion coefficient were selected as input variables. The base layer consisted of support vector regression (SVR), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost), and back-propagation neural network (BPNN), while Ridge regression was used as the meta-learner. Under the current data split, the test set R2, RMSE, MAE, and MAPE of the Stacking model were 0.953, 10.99 m, 8.79 m, and 9.847%, respectively, indicating overall superiority over individual models and other ensemble configurations. The field validation results showed that the relative errors of the model for boreholes LD-1 and LD-2 in the fully mined area were 1.99% and 1.28%, respectively; however, an overestimation of 52.70% occurred for LD-3 in the coal-pillar-adjacent area. This indicates that the model is more suitable for the regional-scale screening of the maximum fractured-zone height and should not be directly used for fine-scale prediction in local boundary-affected zones. SHAP analysis showed that mining thickness, working face length, and hard strata proportion coefficient were the main influencing variables, and their response trends were generally consistent with key-strata control and the transition toward full-mining conditions. This study provides a reference for the rapid prediction of WCFZ height and preliminary evaluation of water-preserved coal mining in weakly cemented mining areas in western China.
Liuwei Sun, Songtao Li, Bo Hu et al.· Processes· 0 citations
Deep excavations are high-risk geotechnical activities, and accurate prediction of diaphragm-wall settlement is important for construction monitoring and deformation control. This study investigates cumulative vertical settlement at 23 diaphragm-wall monitoring points from the deep excavation of Tianjin Goldin Finance 117 in Tianjin, China. Seven prediction models—a naïve persistence model, autoregressive integrated moving average (ARIMA), K-nearest neighbors (KNN), multilayer perceptron (MLP), gated recurrent unit (GRU), Transformer, and XGBoost—were evaluated using a unified five-fold rolling-origin expanding-window validation scheme. GRU achieved the best overall baseline performance, with a mean R2 of 0.9172, a mean absolute error (MAE) of 0.1009 mm, a root mean square error (RMSE) of 0.1341 mm, and a mean absolute percentage error (MAPE) of 0.5875%. GRU was subsequently optimized using the crow search algorithm (CSA), the genetic algorithm (GA), and the whale optimization algorithm (WOA). GRU-WOA achieved the best numerical performance, with a mean R2 of 0.9289 and an RMSE of 0.1215 mm. Relative anomaly levels were further identified from predicted settlement-change rates to characterize temporal concentration and spatial clustering of settlement-change activity. The proposed framework can support priority inspection and targeted monitoring, although the resulting anomaly levels represent project-relative statistical deviations rather than code-based engineering risk classes.
In mining areas of the mountainous regions of Southwestern China, the overlying strata above goafs are mostly thick bedrock with well-developed structural planes. With large-scale exploitation of mineral resources, extensive water- conducting fractured zones (WCFZs) may develop in the overlying strata, frequently inducing water inrush and high-position collapse-sliding hazards. Rapidly estimating the height of the WCFZ (HWCFZ) is therefore critical for the prevention of water hazards and collapse-sliding disasters.
In this study, mining- induced structural overburden was taken as the research object. Based on the instability criterion of the three-hinged arch structure, the influences of mining thickness, strata thickness, joint spacing, broken expansion coefficient, and damage factor on the HWCFZ were analyzed. A simplified fitted prediction model constrained by the three-hinged arch mechanism was established. Using the calculation results of the three-hinged arch mechanical discrimination model as benchmark values, fitting error analysis was conducted through 27 orthogonal tests, and the prediction model was applied to engineering cases in typical mining areas.
The results show that, within the parameter ranges considered in this study, the HWCFZ is approximately linearly and positively correlated with mining thickness, and negatively correlated with strata thickness, joint spacing, and broken expansion coefficient in an inverse-function form, characterized by a rapid decrease at the early stage and subsequent stabilization. In the high-damage range, the HWCFZ shows an accelerated nonlinear increase with increasing damage factor. The sensitivity ranking of the factors affecting the HWCFZ is mining thickness, strata thickness, broken expansion coefficient, joint spacing, and damage factor. The simplified fitted prediction model has a mean absolute error of 3.26 m and a coefficient of determination R2 of 0.96, indicating that the model exhibits good internal consistency with the theoretical three-hinged-arch discrimination model.
When applied to the Faer Coal Mine in Shuicheng, Guizhou, and the Zengziyan Bauxite Mine in Nanchuan, Chongqing, the calculated results are close to those obtained from theoretical formulas, numerical simulations, and centrifuge model tests. The results provide a reference for calculating the HWCFZ induced by ore-seam mining under similar geological conditions.
Jun Li, Jiawei Wan, Xiang Li et al.· Frontiers in Built Environme...· 0 citations
With the rapid development of inland waterway transportation, the demand for reliability and accuracy in hydrological forecasting has been increasing. Inland water levels are affected by rainfall, upstream flood discharge, and seasonal factors, exhibiting highly nonlinear and non-stationary characteristics. Traditional deterministic prediction models can hardly meet the requirements of refined scheduling. This paper proposes a hybrid water level prediction model that integrates Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Radial Basis Function Neural Network (RBFNN), and innovatively introduces an uncertainty quantification mechanism based on the statistic. First, EMD is used to decompose the original complex water level signal into multiple Intrinsic Mode Functions (IMFs) to reduce data nonstationarity. Second, for each IMF component, the PSO algorithm is adopted to globally optimize the centers and spread constants of the RBF neural network, constructing high-precision base prediction models. Finally, the uncertainty coefficient is calculated. Using actual water level data from the Port of Guigang as the experimental object, the results show that the prediction accuracy of the hybrid model reaches R2=0.9415, and the α coefficient can effectively quantify the dynamic risk of prediction results. This study not only provides a high-precision technical approach for inland water level prediction, but its uncertainty quantification results also offer a scientific basis for the reliability evaluation and risk early warning of hydrological forecasting.
Qi Xu, Xiao-Nuo Zhu, Cheng Zeng et al.· International Conference on...· 0 citations
Slope stability assessment remains a fundamental challenge in geotechnical engineering because of the complex nonlinear interactions among soil properties, slope geometry, and hydraulic conditions, particularly variations in pore-water pressure. This study investigates the reliability of Artificial Neural Network–Multi-Layer Perceptron (ANN–MLP) models for predicting the Factor of Safety (FoS) of homogeneous soil slopes through a systematic comparison of three gradient-based optimization algorithms: Adam, Mini-Batch Gradient Descent (MBGD), and Nesterov Accelerated Gradient (NAG). A database comprising 2014 slope cases, compiled from published studies and numerically generated using Limit Equilibrium Method (LEM) and Finite Element Method (FEM) analyses, was used for model development and k-fold cross-validation. Beyond statistical evaluation, the developed models were validated using two classical dry-slope benchmark frameworks based on the Taylor stability charts and Bishop–Morgenstern stability coefficients, followed by two documented engineering case studies from Hulu Kelang and Pahang, Malaysia, to assess predictive performance under both dry and variable hydraulic conditions. Adam achieved the highest cross-validated predictive accuracy (R2 = 0.988; RMSE = 0.212), whereas MBGD demonstrated the closest overall agreement with the reference LEM solutions across the validation cases and under increasing pore-water pressure ratios. NAG generally produced more conservative predictions while exhibiting greater sensitivity to hyperparameter selection. All models successfully reproduced the expected nonlinear reduction in FoS with increasing pore-water pressure, consistent with established geotechnical behaviour. The results demonstrate that optimizer selection significantly influences ANN–MLP prediction behaviour and that properly validated gradient-based ANN models can serve as efficient decision-support tools for rapid slope stability assessment under hydraulic variability.
Shaza Soleiman, M. Rahhal· Geotechnics· 0 citations
In response to the problems such as large surface subsidence, strong overburden damage, low resource recovery rate, and prominent environmental constraints in underground coal mining under buildings, railways, and water bodies, this paper takes the filling coal mining technology as the core and constructs an integrated research framework of “equivalent mining height theory-FLAC3D numerical simulation-multi-objective parameter optimization”. It analyzes the influence of filling rate, filling body elastic modulus, filling body compressive strength, and advancement step distance on surface movement, overburden damage height, and stress distribution in the mining area. The simulation results show that compared with the traditional caving method, when the filling rate increases from 70% to 95%, the maximum surface subsidence value decreases from 684 mm to 54 mm, the reduction rate increases from 45.1% to 95.7%; the height of the water-conducting fracture zone of the overburden decreases from 78.6 m to 21.8 m, a reduction of 72.3%; the stress concentration coefficient in front of the working face decreases from 2.31 to 1.43. Considering both comprehensive safety control and economic cost, it is recommended to adopt the optimized scheme of “filling rate 90%, filling body compressive strength 3.5 MPa after 28 days, advancement step distance 1.2 m, filling lag distance less than 6 m”. This scheme can control the maximum surface subsidence to 96 mm, increase the resource recovery rate to 87.5%, and reduce the unit coal mining comprehensive cost by 13.6% compared with the high-strength filling scheme of 95%. The research results show that the combination of filling coal mining and numerical simulation optimization can provide a quantifiable design basis for safe, efficient, and green mining of “three underground” coal.
Biao Liu, Haibo Zhang, Xiaofeng Yang et al.· Journal of Physics, Conferen...· 0 citations
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