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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]

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]

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]

• 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]