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A key issue for effective management and operating of dam reservoirs is predicting the water inflow values into dam reservoir. To address this subject, here, genetic programming (GP) is used by proposing two cases. In the first case, water inflow values are predicted separately for each month. However, in the second case, these values are predicted simultaneously for all months. Furthermore, for each case, two approaches are proposed here. In the first approach, the hybrid method, called the ANN-NGSA-II method, is proposed to find proper input data sets. However, in the second approach, the useful input data sets are found automatically using the GP method. For comparison purpose, the ANN and SARIMA models are also used, to predict the water inflow values. As a case study, in this research, the Zayandehroud dam reservoir is selected. The results indicate that the ANN model outperforms both results of the GP and SARIMA methods. In other words, correlation coefficient (R2), Nash Sutcliffe (NS), and root means square error (RMSE) values of ANN are 0.97, (0.88), 0.954 (0.87), and 17.19 (30.54) million cubic meters, respectively, for training (test) data set. [ABSTRACT FROM AUTHOR]

Fractures are widely distributed in karst areas, and when flow rates are high, they exhibit complex nonlinear behavior that cannot be accurately described by Darcy's law. In this work, a hydro-mechanical coupling model based on a discrete fracture network is proposed to predict tunnel water inflow, accounting for the impact of non-Darcy flow. The model's feasibility has been validated by comparing it with experimental results and the field measurements of flow rates at the Bodaoling Tunnel in Guizhou, China. The results show that Darcy flow tends to overestimate water inflow by approximately 25% compared to non-Darcy flow. The non-Darcy effect grows with the increase in initial fracture width and empirical constant q. When q exceeds 8.77 × 10−6, the growth rate of the Forchheimer number along the fracture width slowed down, and the inhibitory effect of non-Darcy flow on flow became gentle. Additionally, in a complex fracture network, the inflow rate limited by non-Darcy flow at one point drives the water flow through a connect fracture to another point, which increases the difficulty in water inflow prediction. This work highlights the importance of non-Darcy flow and fracture networks when accurately predicting water inflow in tunnels. [ABSTRACT FROM AUTHOR]

The failure characteristics of roof strata in coal mining and the accurate prediction of mine water inflow are prerequisites for mine safety. Taking a typical coal mine in Southwest China as a case, a new type of similar material suitable for coal mine in exposed karst areas was developed. Through the physical and numerical simulation, the failure characteristics of overlaying rock in karst areas were analyzed. The results showed that the plastic zone developed and accumulated from the roof. After mining, the stress and displacement of overlying rock were divided into three areas along the mining direction. The middle area experienced the maximum change. Overall, the height of falling and water-conducting fracture zones in karst exposed areas was slightly lower than that in other areas. And then, the hydrogeological structure considering the water-conducting fracture zone was rationally generalized, and a three-dimensional numerical model for predicting mine water inflow was built. It was found that the numerical simulation method had the minimum error and the maximum accuracy than other traditional methods, and its results were the closest to the measured value, which could more truly reflect the dynamic formation process of mine water inflow. [ABSTRACT FROM AUTHOR]

According to the special form of water occurrence and migration in No.2 coal seam of Balasu Coal Mine in northern Yuheng Mining Area of Jurassic Coal Field in northern Shaanxi province, through theoretical analysis, calculation and prediction, simulation experiment and other methods, the water resources volume, water-rich zoning and mine water inflow are studied, and the water characteristics of No.2 coal seam water in the study area are preliminarily explored. According to the five main control indexes of coal seam thickness, coal core extraction rate, coal seam porosity, seam roof aquifer thickness and bottom water-insulating layer thickness affecting the water-rich coal seam, ArcGIS software and analytic hierarchy process (AHP) were combined to make water-rich prediction zoning for the No.2 coal seam in the study area. The water inflow of underground driving (including shaft, roadway) and stoping face under different working conditions is predicted. The results show that the estimated hydrostatic reserves of No.2 coal seam in the study area are about 0.665 billion m3, the dynamic recharge water is about 362 m3/h, and the coal seam water shows an overall decaying trend. The water-rich zone of No.2 coal seam is divided into 5 types: relatively strong, strong, medium, weak and weak.

Fractures are widely distributed in karst areas, and when flow rates are high, they exhibit complex nonlinear behavior that cannot be accurately described by Darcy’s law. In this work, a hydro-mechanical coupling model based on a discrete fracture network is proposed to predict tunnel water inflow, accounting for the impact of non-Darcy flow. The model’s feasibility has been validated by comparing it with experimental results and the field measurements of flow rates at the Bodaoling Tunnel in Guizhou, China. The results show that Darcy flow tends to overestimate water inflow by approximately 25% compared to non-Darcy flow. The non-Darcy effect grows with the increase in initial fracture width and empirical constant q. When q exceeds 8.77 × 10−6, the growth rate of the Forchheimer number along the fracture width slowed down, and the inhibitory effect of non-Darcy flow on flow became gentle. Additionally, in a complex fracture network, the inflow rate limited by non-Darcy flow at one point drives the water flow through a connect fracture to another point, which increases the difficulty in water inflow prediction. This work highlights the importance of non-Darcy flow and fracture networks when accurately predicting water inflow in tunnels.

During tunnel construction in strongly developed karst terrain, water inrush hazards often occur due to the complex hydrogeological conditions, which require accurate prediction of water inflow. In this study, a dynamic modeling approach for water inflow prediction of karst tunnels using the conduit flow process (CFP) is developed that considers both karst duality and changing boundary conditions of the tunnel. The CFP model has a good agreement with field-observed hydraulic head after calibration, and the Nash–Sutcliffe model efficiency (NSE) for the CFP model is 97.3%. Numerical calculation of water inflow was conducted in a successive drilling scenario with permeability change of the surrounding rocks. Additionally, a modular three-dimensional finite-difference ground-water flow model (MODFLOW) has been applied to predict the water inflow, for comparison with the CFP model. The prediction results obtained from the CFP model are generally in close agreement with the field-observed results; the percentage errors were 13.3% and 5.4%, respectively. For the MODFLOW model, the percentage errors were 34.2% and 36.8%, respectively. The proposed CFP model is both closer to reality and more reasonable than the MODFLOW model in predictive analysis of water inflow into karst tunnels, reflecting the influence of karst conduits on the water inflow process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The electricity consumption of mine multi-level relay drainage system is very large, in order to improve economy of drainage system operation, it is necessary to optimize drainage system control, but current optimal control methods of mine multi-level relay drainage system have some problems such as easy overflow of sump and frequent start and stop of water pump. For the above problems, an optimal control strategy of mine multi-level relay drainage system was proposed. The optimal control model of mine multi-level relay drainage system was established which took water level of each level sump must be between the lowest and highest level and displacement of each level sump should not be less than inflow as constraint conditions, and the lowest sum of electricity costs of each level within one drainage period as objective function. By introducing margin into flow constraint condition, frequent start and stop of water pump is avoided. Gray model is used to predict water inflow, so as to obtain accurate water level of sump and avoid overflow accident. The application results show that the optimal control strategy can reduce electricity cost of drainage system under premise of ensuring safety of drainage.

The prediction of water inflow from coal mining face is the basis for the prevention and control of water in the production process of coal mines. Through the generalization of hydro-geological conditions in coal mine goaf, combined with the analysis of hydro-geological models of various sources and sink items in groundwater numerical simulation software Visual MODFLOW, it is proposed that the water gushing in goaf should be generalized into the boundary conditions of the drainage ditch with the characteristics of planar drainage in the process of numerical simulation. Using the boundary of the drainage ditch and the analysis of the local water balance, the amount of water in the goaf after the mining of the working face can be calculated. Based on the generalized method of goaf in this study, the numerical simulation of water inflow in 303 working face of Tingnan Coal Mine in Huanglong Coalfield is carried out. The results show that the method is reasonable and reliable, and the flow field characterization and water prediction results are better than that from the model of a hole pumping well.

To construct an accurate and stable approach for water inflow forecasting, a series of advanced and effective techniques, such as variational mode decomposition (VMD), outlier robust extreme learning machine (ORELM) and multi-objective grey wolf optimizer (MOGWO), are appropriately integrated into this study. Considering that the influence of the mode number on the VMD decomposition effectiveness, such an argument is determined by observing the converged centre frequency distribution among the components. Then the characteristic items of water inflow series are extracted by VMD, thus obtaining a series of sub-components. Afterwards, ORELM is applied to predict each component, where the parameters of ORELM are optimized by MOGWO with multi-objective functions including forecasting accuracy and stability. Correspondingly, the aggregation of all components' prediction values is considered as the final results. The experimental results obtained by performing eight various models on real-time data demonstrate that the supplementary modules achieve positive effects on the improvement of prediction accuracy, where the proposed model implements an average performance promotion of 48.43% compared with the contrastive models.

During tunnel construction in strongly developed karst terrain, water inrush hazards often occur due to the complex hydrogeological conditions, which require accurate prediction of water inflow. In this study, a dynamic modeling approach for water inflow prediction of karst tunnels using the conduit flow process (CFP) is developed that considers both karst duality and changing boundary conditions of the tunnel. The CFP model has a good agreement with field-observed hydraulic head after calibration, and the Nash–Sutcliffe model efficiency (NSE) for the CFP model is 97.3%. Numerical calculation of water inflow was conducted in a successive drilling scenario with permeability change of the surrounding rocks. Additionally, a modular three-dimensional finite-difference ground-water flow model (MODFLOW) has been applied to predict the water inflow, for comparison with the CFP model. The prediction results obtained from the CFP model are generally in close agreement with the field-observed results; the percentage errors were 13.3% and 5.4%, respectively. For the MODFLOW model, the percentage errors were 34.2% and 36.8%, respectively. The proposed CFP model is both closer to reality and more reasonable than the MODFLOW model in predictive analysis of water inflow into karst tunnels, reflecting the influence of karst conduits on the water inflow process.

Affected by the coupling of excavation disturbance and ground stress, the heterogeneity of surrounding rock is very common. Presently, treating the permeability coefficient as a fixed value will reduce the prediction accuracy of the water inflow and the external water pressure of the structure, leading to distortion of the prediction results. Aiming at this problem, this paper calculates and analyzes tunnel water inflow when considering the heterogeneity of permeability coefficient of surrounding rock using a theoretical analysis method, and compares with field data, and verifies the rationality of the formula. The research shows that, when the influence of excavation disturbance and ground stress on the permeability coefficient of surrounding rock is ignored, the calculated value of the external water force of the tunnel structure is too small, and the durability and stability of the tunnel are reduced, which is detrimental to the safety of the structure. Considering the heterogeneity of surrounding rock, the calculation error of water inflow can be reduced from 27.3% to 13.2%, which improves the accuracy of water inflow prediction to a certain extent. [ABSTRACT FROM AUTHOR]

The global impact of coal mining on groundwater resources is increasingly prominent. Constructing waterproof curtains can mitigate the destruction caused by open-pit coal mining on groundwater resources. However, the construction of these curtains alters the hydrogeological conditions, leading to abrupt changes in mine water inflow that are challenging to predict. Traditional models like Long Short-Term Memory Neural Network (LSTM) and Gated Recurrent Unit Neural Network (GRU) demonstrate poor performance when predicting abrupt changes in data trends. This study proposes a multi-model prediction approach and a Self-Attention-based prediction model to address this issue. The proposed Approach, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), leverages the strengths of multiple models for prediction. The research results found that the proposed Self-Attention model excels at forecasting abrupt shifts in data trends. Comparatively, the multi-model predictions show a 26.12 % improvement in Mean Absolute Error (MAE) and a 33.47 % improvement in Root Mean Square Error (RMSE) when compared to single-model predictions. This study not only unveils the potential of Self-Attention in predicting abrupt trend changes but also integrates it with traditional LSTM and Support Vector Machine (SVM) models. The multi-model prediction framework proposed in this study integrates the strengths of multiple models, achieving higher accuracy than a single model. Accurate prediction of mine water inflow is essential, as it can provide valuable insights for decision-making by coal mining enterprises and local regulatory authorities, offering a scientific basis for effective management. • A novel multi-model prediction framework is introduced. • An prediction model based on Self-Attention is proposed. • Uncovering Self-Attention's untapped trend forecasting potential. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Affected by the coupling of excavation disturbance and ground stress, the heterogeneity of surrounding rock is very common. Presently, treating the permeability coefficient as a fixed value will reduce the prediction accuracy of the water inflow and the external water pressure of the structure, leading to distortion of the prediction results. Aiming at this problem, this paper calculates and analyzes tunnel water inflow when considering the heterogeneity of permeability coefficient of surrounding rock using a theoretical analysis method, and compares with field data, and verifies the rationality of the formula. The research shows that, when the influence of excavation disturbance and ground stress on the permeability coefficient of surrounding rock is ignored, the calculated value of the external water force of the tunnel structure is too small, and the durability and stability of the tunnel are reduced, which is detrimental to the safety of the structure. Considering the heterogeneity of surrounding rock, the calculation error of water inflow can be reduced from 27.3% to 13.2%, which improves the accuracy of water inflow prediction to a certain extent.

Grouting prevents groundwater leakage into tunnels, based on the exponent model that expresses the nonlinear variation of the hydraulic conductivity of the surrounding rock, the formulas for predicting the magnitude of water inflow and outer water pressure of the lining after grouting are deduced. The parameter analysis shows how the water inflow decreases as the hydraulic conductivity of the grouting circle diminishes and the thickness of the grouting circle increases. When the parameter α (attenuation coefficient), which expresses the decreasing amplitude of the permeability coefficient of the surrounding rock with depth, is greater than 0, the water inflow increases until it reaches a maximum at a certain depth, and then the inflow decreases to 0 if deep enough. After considering the variation in the hydraulic conductivity of the surrounding rock, the thickness and hydraulic conductivity of the grouting circle may be designed to be too large to reduce the magnitude of the water inflow. Meanwhile, to reach the limited drainage criterion of the tunnel groundwater, the grouting circle thickness decreases gradually as α increases after the nonlinear variation of the hydraulic conductivity of the surrounding rock, which can reduce the cost for plugging the groundwater. Thus, it is critical to consider hydraulic conductivity variation during water inflow predicting and grouting when designing tunnels. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the uncertainty and randomness in the process of risk assessment of water inrush, 10 factors of hydrogeological conditions were considered to construct evaluation index system, and a cloud model for water inrush risk assessment in coastal gold mine was established. Based On the evaluation resultst the phase space reconstruction of the water inrush time series is carried out. The evolution law of the influx phase distance is analyzed, and the early warning mechanism of water inrush is established. Combined with the reconstructed phase space, the RBF neural network prediction model is established. The results show that the evaluation results of the cloud model are accurate and reliable. The phase space reconstruction reveals the chaotic characteristics of the system. The nearest neighbor phase evolution magnifies the subtle characteristics of the water inflow change, which provides a basis for the establishment of the early warning mechanism of the water inrush. The chaotic RBF nerve network short-term accurate predictions of water inflow and provides technical support for safe underground mining. [ABSTRACT FROM AUTHOR]

Due to the extremely complicated hydrogeological environment, significant symptoms of water inrush can not be detected accurately using normal exploratory methods, which produces hundreds of water inrushes occurred during tunnel construction in karst area. This study aims to present a new water inflow prediction technique without considering the relationship between hydrogeological features and water discharge rate. Therefore, the nonlinear regression Gaussian process analysis is applied to develop a model for predicting water inflow into tunnels. In order to meet the requirement of the data format of Gaussian process regression model (GPR), the basic evaluation index system of water inflow into tunnels and corresponding criterion are set up and quantified based on the statistical information of water inrush cases. To verify its feasibility, The GPR model is applied to Zhongjiashan tunnel on Jilian highway in China. The results of the comparisons indicate that the prediction results obtained from the GPR model are generally in a good agreement with the field-observed results. The proposed Gaussian process, on the whole, performs better than the support vector machine (SVM) and artificial neural network (ANN) in predictive analysis of water inflow into tunnels. [ABSTRACT FROM AUTHOR]

[Display omitted] • The random deterministic three-dimensional fracture coupling model is constructed. • A new fracture search algorithm: full space parallel search algorithm is proposed. • Based on this, the accurate prediction of water inflow of the tunnel is realized. With the rapid development of national infrastructure construction, the water inrush disaster of "strong burst, high water pressure and large flow" is more and more frequent in the construction process. To achieve high-precision prediction of tunnel water inflow, based on the Yue Longmen Tunnel, according to the structural plane detection data in the study area, combined with the Monte Carlo algorithm and using "parent-daughter" and "step–structure" correction mode, this paper constructs a random deterministic three-dimensional fracture network seepage model. Based on the three-dimensional fracture network seepage model and the principle of flow balance, the water inflow of each mileage section of the Yue Longmen Tunnel is solved by numerical calculation method, and good results are obtained through comparison and verification. The research results have important theoretical significance and engineering application value for tunnel water inflow prediction. [ABSTRACT FROM AUTHOR]

• Permeability coefficients are obtained based on water inflow in probe drilling holes. • Formulas of the water inflow are derived based on the superposition principle. • Factors affecting the water inflow in subsea tunnels are analyzed. • Prediction formulas of water inflow are verified by field test and numerical simulation. Prediction of water inflow is a key scientific problem in construction of subsea tunnels. Tsingtao subsea tunnel is taken as the research object, and a calculation method for predicting and analyzing the water inflow based on superposition principle is proposed by applying theoretical analysis, numerical simulation and field monitoring. First of all, the computation model of seepage field and water inflow in the subsea tunnel is established. The permeability coefficients of strongly weathered layer and moderately weathered layer are determined according to the permeability coefficient of slightly weathered layer. Secondly, based on the well flow model of infinite confined aquifer, the double water gushing points calculation model is presented, and then the model is simplified into single water gushing point calculation model. The equivalent calculation formulas of water inflow under the condition of multi-horizontal strata are derived. The water inflow which is affected by the tunnel depth, radius and seawater depth is analyzed. Thirdly, the proposed method is compared with the engineering practice and numerical simulation, which verifies the accuracy and feasibility of the method. The results show that: (1) The water inflow of subsea tunnels increases with the permeability coefficients of strongly and moderately weathered layers. (2) The water inflow of subsea tunnels first decreases and then increases with the tunnel depth increasing. (3) The water inflow of subsea tunnels increases linearly with the tunnel radius and seawater depth. [ABSTRACT FROM AUTHOR]