Prediction Of Water Inflow Research Articles

Probabilistische Prognose von Karstwasserzutritten beim Bau von Untertagebauwerken

AbstractVarious methods have been developed in recent decades to predict hazards associated with karst voids in underground construction. Common to all these methods is that the predicted range of water inflow is often insufficient for the purpose of implementing the planned construction works. This is usually due to an incomplete knowledge of the karst conduit system within a project area, making it difficult to predict the position and characteristics of karst voids. The method presented in this paper permits a robust prediction of karst water inflow. It is based on a combination of stochastically generated, pseudo‐genetic karst conduit systems and hydraulic modelling of the hydrogeological conditions using a Monte Carlo approach. This approach facilitates a plausible estimation of the expected range of karst‐induced water inflows and also enables the probability of encountering a karst voids. to be determined. The predictions allow for differentiated treatment of the hazards associated with karst water during the construction and operation phase of underground structures. In concrete terms, this relates to the planning and implementation of exploratory measures and ground‐improvement measures, the design of the dewatering system and its monitoring during the construction and operation phase.

An upscaling approach to predict mine water inflow from roof sandstone aquifers

Underground coal mining suffers from groundwater intrusion from the aquifers overlying coal seams. Therefore, developing methods for the accurate prediction of roof water inflow is urgently needed to design a safe drainage system. In this study, we developed a novel upscaling framework to predict roof water inflow by integrating the multiscale hydrogeological properties of roof aquifers. In this framework, we imaged rock samples via scanning electron microscopy and performed pore-scale analysis based on fractal theory. A fractal model of permeability was introduced to calculate the seepage capacity of the pore structure in the samples. The effect of fractures was further evaluated via core-scale pneumatic experiments. Subsequently, we derived an upscaling formula of hydraulic conductivity used for predicting roof water inflow at the field scale. The proposed upscaling approach was demonstrated using data from a coal mine in Northern China. The results indicate that the actual water inflow (21 m3/h) is within the predicted range of our upscaling framework (9.32–92.78 m3/h), and the initial line fracture rate dx is distributed between 0.02 % and 0.03 %. Therefore, these findings can guide the development of methods for considering micropores and fractures simultaneously and scaling them up to the field scale for effective prediction of water inflow from roof aquifers.

A Dynamic Modeling Approach to Predict Water Inflow during Karst Tunnel Excavation

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.

Development of Medium-Term Water Inflow Forecasting Models for Planning Electricity Generation in Isolated Power Systems

Reliable operation of power systems with a significant share of hydropower plants (HPPs) in the energy mix depends in many respects on how accurately the water inflow is forecasted. Taken together, the water inflow prediction and optimal planning of production define energy security, ensure the possibility of protection from floods, and eliminate idle discharges at hydroelectric power plants. The solution of such problems is complicated by lack of reliable information about the water inflow, its being stochastic in nature, a variable electricity consumption pattern, and inaccurate prediction and planning models. Improvement of prediction accuracy is focused on determining the water inventory for planning prospective electricity generation at HPPs taking into account regulation in the medium term. Such regulation is necessary to meet the power system load in the load curve peak and semi-peak parts. The paper considers the problem of constructing a medium-term water inflow prediction model for planning electricity generation for a week ahead taking into account climate changes in isolated operating power systems taking as an example the electric power systems of the Gorno-Badakhshan autonomous oblast in Tajikistan. For taking into account constant climate changes, it is proposed to use an approach based on machine learning methods, which features a self-adaptation capability. Based on the results of accomplished experimental and industry-grade numerical analyses, the expediency of using a model based on an ensemble of regression decision trees has been shown.

Prediction of Water Inflow into Tunnel Crossing Intersecting Faults Based on IDB Seepage Model

Prediction of Water Inflow into Tunnel Crossing Intersecting Faults Based on IDB Seepage Model

Dynamic and Static Reserve Recharge Characteristics of a Dewatering Well Seepage from an Aquifer Bottom by Sand Tank Seepage Experiment

Ground water inflow from an overlying aquifer happens frequently in underground engineering; for properly controlling ground water during construction, the most important tasks are to assess the possibility and the probable water inflow, which depends on the ground water recharge characteristics. However, less work has been carried out on this topic. In order to study the water inflow variation characteristics including dynamic replenishment and static reserve release from a submersible aquifer, sand-tank seepage experiments on a dewatering well seepage from an aquifer bottom under the conditions of different water pressure heads insides the dewatering well ( P ) were designed and performed. The results show that (1) when the dewatering beginning, the water inflow increases to a peak value rapidly and then decreases following a first-order exponential decay function. In the initial phase, the water inflow is mainly composed of static reserve release, which decreases quickly with time also following a first-order exponential decay function. Meanwhile, the recharge of dynamic replenishment quickly increases with time following a first-order exponential growth function, which mainly forms the water inflow in the quasi-steady state. (2) The equal time of dynamic and static reserve release and the quasi-steady state time both present a first-order exponential decay function with the decrease of P . (3) The peak water inflow should perform at the beginning of dewatering which presents a little late after the beginning of dewatering and increases with the decreasing of P following a linear function. The results will play important references for water inflow prediction and calculation from overlying aquifers for underground projects.

The Deviation of Semi-Theoretical and Semi-Empirical Water Inflow Prediction Formula Based on Sphere Coordinate Conversion and a Grouting Excavation Evaluation Method

The water in the rock medium is exchanged with the confined aquifer through the fracture, which leads to the water inflow line in the confined aquifer is no longer horizontal. This paper assumes that the aquifuge is a kind of semi-isolation layer, while the first-order derivative of the total head slope line function within the influence of precipitation approaches the slope of the line connecting the top plate of the aquifuge with the spherical center. This hypothesis demonstrates the relationship between the bottom of the well water inflow and the complete well gushing water. Laplace’s equation for the spherical coordinate transformation is used to find the analytical solution of the water inflow for stable flow. The calculation results are in line with reality through actual engineering and numerical simulation methods. The current numerical simulation methods and theoretical methods mostly consider the aquifer in the ideal state, which is difficult to simulate the fractured rock mass. The theoretical formula proposed in this paper can more effectively reflect the actual seepage situation of fractured rock mass than other formulas. In addition, the combination of theoretical derivation, numerical simulation and field measurement can predict the water inflow more accurately than unilateral research. At the same time, for the question of whether the face excavation is grouted or not, this paper using the subjective and objective assignment weight method combined with analytic hierarchy process method and entropy-weight method to take the weight calculation and giving a slurry excavation judgment method based on the proposed formula. Theoretical support is given for the selection of permeability coefficients for each hole in the overrun exploration and this method is validated by different projects, which has some degree of reference value.

Chaos-generalized regression neural network prediction model of mine water inflow

Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension (M) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices (R, RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.

Analytical modeling of mine water rebound: Three case studies in closed hard-coal mines in Germany

Purpose.In this paper we present and validate an analytical model of water inflow and rising level in a flooded mine and examine the model robustness and sensitivity to variations of input data considering the examples of three closed hard-coal mines in Germany. Methods. We used the analytical solution to a boundary value problem of radial ground water flow to the shaft, treated as a big well, and water balance relations for the series of successive stationary positions of a depression cone to simulate a mine water rebound in the mine taking into account vertical distribution of hydraulic conductivity, residual volume of underground workings, and natural pores. Findings. The modeling demonstrated very good agreement with the measured data for all the studied mines. The maximum relative deviation for the mine water level during the measurement period did not exceed 2.1%; the deviation for the inflow rate to a mine before its flooding did not exceed 0.8%. Sensitivity analysis revealed the higher significance of the residual working volume and hydraulic conductivity for mine water rebound in the case of thick overburden and the growing significance of the infiltration rate and the flooded area size in the case of lower overburden thickness. Originality.The developed analytical model allows realistic prediction of transient mine water rebound and inflow into a mine with layered heterogeneity of rocks, irregular form of the drained area, and with the inflow/outflow to a neighboring mine and the volume of voids as a distributed parameter without gridding the flow domain performed in numerical models. Practical implications.The study demonstrated the advantages of analytical modeling as a tool for preliminary evaluation and prediction of flooding indicators and parameters of mined out disturbed rocks. In case of uncertain input data, modeling can be considered as an attractive alternative to usually applied numerical methods of modeling ground and mine water flow.

Study on Water Inflow Variation Law of No.1 Shaft Auxiliary Shaft in HighLiGongshan Based on Dual Medium Model

When the fracture is not developed and the connectivity is poor, the original single medium simulation cannot meet the accuracy requirements. Now, the seepage simulation of fractured rock mass has gradually developed from equivalent continuous medium to dual medium and multiple medium. However, it is still difficult to establish the connection between a discrete fracture network model and a continuous medium model, which makes it difficult to simulate the influence of fracture location on the seepage field of rock mass. As the excavation direction of the shaft is vertically downward, the surrounding strata are symmetrical around the plane of the shaft axis, which is different from the horizontal tunnel. Taking the auxiliary shaft of the No.1 Shaft in HighLiGongshan as the engineering background, combined with Monte Carlo methods and DFN generator built in FLAC3D5.01, a discrete fracture network is generated. Based on the dual medium theory, MIDAS is used to optimize the modeling of each fracture group. At the same time, the concept of “Fracture Weakening area” is introduced, and the simulation is carried out based on a fluid–solid coupling method. It is found that the simulation effect is close to the reality. The water inflow increases with the increase of shaft excavation depth, and the water inflow at the end of excavation is nearly three times larger than the initial value. Combined with Legendre equation, a new analytical formula of water inflow prediction is proposed. It is found that this analytical formula is more sensitive to permeability and has a greater safety reserve.

Analysis of Tunnel Water Inrush Considering the Influence of Surrounding Rock Permeability Coefficient by Excavation Disturbance and Ground Stress

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.

贵州德江隧道区岩溶蓄水构造特征及涌水量预测

贵州德江隧道区岩溶蓄水构造特征及涌水量预测

RF-CARS Combined with LIF Spectroscopy for Prediction and Assessment of Mine Water Inflow

RF-CARS Combined with LIF Spectroscopy for Prediction and Assessment of Mine Water Inflow

Prediction of water inflow into a tunnel based on three-district zoning of faults

Water inflow into a tunnel subject to fault zones and fault effective zones is interpreted in this study using a three-district zoning method. An improved Brinkman model is proposed to investigate the seepage law of the geological defects of a fault. A coupled calculation model of typical Darcy seepage and Brinkman rapid seepage is constructed to establish a mathematical model of nonlinear seepage flow based on three-district zoning and the corresponding numerical implementation. In addition, the influence of the angle between the fault dip and tunneling directions on the water inrush in a tunnel is also studied. The variation characteristics of the pore pressure and velocity fields under various construction stages are analyzed, as is the seepage velocity of pore water at the tunnel heading. Moreover, the effects of the fault dip direction on the seepage field and water inflow are discussed. The following results were obtained. (1) Before excavation to the fault effective zone, the pore pressure at the same depth remained essentially unchanged in front of the tunnel heading. After excavation to the fault effective zone, the pore pressure at the same depth increases linearly with distance in front of the tunnel heading. (2) The seepage velocity changed gently before excavation to the fault effective zone but showed wavy fluctuating after excavation to the fault effective zone. The velocity reached its maximum value in the fault zone before decreasing rapidly and remaining close to zero in the general surrounding rock zones. (3) As the excavation length increased, the water inflow increased gradually, reaching its maximum value in the fault zone. (4) In the fault zone, the seepage velocity decreased with an increase in the fault dip. The water inflow reached maximum and minimum values at angles of 90° and 150°, respectively.

The modification and Application of Existing Empirical Formula for Predicting Water Inflow Considering the Space of Caverns

The prediction of water inflow in cavern is significant for underground engineering, as the inflow has important influence on construction and drainage system. At present, the empirical formulas for predicting inflow are usually based on a single cavern, but the influence of nearby caverns is not taken into account. In order to consider the influence of spacing and radius on water inflow, the inflow in an underground crude oil storage cavern is analyzed by numerical analysis in this paper. The results show that when the s/r < 20, the spacing has great influence on the inflow; when 20<s/r<60, it has some effect on the inflow; and when s/r>60, it almost has no effect on the inflow (s and r represent spacing between caverns and radius). Based on numerical analysis, empirical formulas for predicting water inflow are modified, and the new formula is proposed considering the influence of the spacing. Finally, field data show that the new formula is more suitable for predicting inflow.

Prediction of water inflow from fault by particle swarm optimization-based modified grey models.

Water inflow from fault (WIF) and its secondary impacts are the main environmental challenges in the mining industry. Traditional prediction methods for WIF are exceedingly challenging and costly. In this article, two exponentially weighted moving average (EWMA) modified grey models (GMs, i.e., EGM and REGM) were established to predict the WIF. Particle swarm optimization (PSO) algorithm was employed to optimize parameters of the models. Based on actual WIF data from Buliangou coal mine, the optimized models (i.e., EGM-PSO, REGM-PSO) were used to obtain the prediction equations for WIF. To investigate the validity of the proposed models, the differences between actual values and predicted values were analyzed, and comparison results were obtained by the commonly used GM and GM-PSO. Results show that, for the sample with the larger initial particle swarm and smaller inertia weight, there is a faster convergence speed of the PSO algorithm. Particle search efficiency in the PSO-optimized EWMA-GM is higher than that in the GM-PSO. Through the predicted results of WIF, it is found that the REGM-PSO is the best choice for WIF prediction, and the more historical information, the higher the predicted accuracy. Besides, the parameter optimization by the PSO, the EWMA optimization method and optimization of residuals all can improve the predicted accuracy. Predicted results also show that WIF will have a substantial growth in the future. Therefore, reasonable measures (e.g., draining and grouting) need to be taken to mitigate the damage caused by fault water inflow.

Identification of rock pore structures and permeabilities using electron microscopy experiments and deep learning interpretations

Permeability is an important hydrogeological parameter for the quantitative evaluation of water resources and prediction of water inflow. In this study, we examine a typical water-bearing sandstone obtained in North China to explore the correlation between the microscopic pore characteristics and macroscopic permeability of the sandstone. In addition, pixel-level annotated data are generated from the images obtained in electron microscopy experiments for deep learning training. Using the deep learning framework, we analyse the pore characteristics through semantic image segmentation based on artificial intelligence and explore the relationship between the microscopic pore characteristics and the macroscopic permeability parameters of the sandstone. This method addresses the limitations of traditional image recognition methods, such as the inability to obtain the complete pore space characteristics in scanning electron microscopy (SEM) images as well as poor segmentation and low accuracy. Moreover, this method can be used to realise the full benefits of accurate image recognition, and it enables the automatic processing of microscopic images to significantly improve the accuracy of pore identification in rock samples.

Prediction of water inflow in Tsingtao subsea tunnel based on the superposition principle

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.

A Gated Recurrent Unit Network Model for Predicting Open Channel Flow in Coal Mines Based on Attention Mechanisms

The prediction of water inflow during coal mining is an important issue. There are many factors that can affect the water inflow in mines. The intercoupling of these factors makes it difficult for current water inflow forecasting methods to meet the needs of real-time forecasting. Open channels are the main devices used for mine drainage, and their flow rate reflects the water inrush of a mine to some extent. This paper uses a hybrid neural network model combining attention mechanisms and a gated recurrent unit network to make real-time predictions of open channel flow. First, attention mechanisms are used to learn the interdependence between multisource hydrosensor data, and then, a gated recurrent unit network is employed to capture the dependencies on different time scales to improve the prediction accuracy of the neural network model. Finally, we design a series of comparative experiments to verify and analyse the performance of the hybrid neural network model. The experimental verification shows that the proposed model can learn the dependency relationships among multisource sensors, and the modelling of these dependencies can greatly improve the prediction accuracy of real-time flow in open channels.

浅谈矿井涌水量预测的常见方法

浅谈矿井涌水量预测的常见方法