Dynamic prediction of water inflow in mountain tunnels based on non-Darcian flow

Water inflow into mountain tunnels exhibits high variability and nonlinear seepage behavior, leading to significant prediction inaccuracies and poor pattern recognition when conventional analytical methods are applied. This study proposes a dynamic water inflow prediction method specifically designed for mountain tunnels. The method is based on groundwater dynamics theory, employing non-Darcian law as the governing equation and deriving analytical solutions applicable to both confined and phreatic aquifer conditions. The method incorporates spatiotemporal variations along the tunnel alignment, enabling both short-term and long-term dynamic predictions of water inflow. The study examines the nonlinear characteristics of the seepage field during tunnel water inrush. The research findings indicate that the predictive results are consistent with the hypothesized two-stage water inflow pattern, with relative errors for key parameters, such as maximum water inflow, normal water inflow, and duration of water inflow, remaining within 10%. The magnitude of water inflow is positively correlated with the permeability coefficient, head height; it is negatively correlated with the axial distance to the tunnel face and the non-Darcian influence coefficient. Both water inflow and water pressure are subject to non-Darcian effects within a defined influence zone extending approximately 1.3 times the tunnel diameter. Comparisons with established predictive methods, numerical simulations, and data from existing tunnel projects confirm the effectiveness of the proposed method. Moreover, the method was successfully applied to a mountain tunnel in the Tibet Plateau region in southwestern China, where it achieved prediction errors within 3% to 8%, demonstrating high reliability.