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Prediction of water inflow into underground excavations in fractured rocks using a 3D discrete fracture network (DFN) model
Authors:E Karimzade  M Sharifzadeh  H R Zarei  K Shahriar  M Cheraghi Seifabad
Institution:1.Department of Mining Engineering,Isfahan University of Technology,Isfahan,Iran;2.Department of Mining and Metallurgy Engineering,Western Australian School of Mines, Curtin University,Bentley,Australia;3.Mahab Ghods Consulting Engineering Company,Tehran,Iran;4.Department of Mining and Metallurgical Engineering,Amirkabir University of Technology,Tehran,Iran
Abstract:Groundwater flow is a major issue in underground opening in fractured rocks. Because of finding the fracture connectivity, contribution of each fracture in flow, and fracture connectivity to excavation boundary, the prediction of water flow to underground excavations is difficult. Simulation of fracture characteristics and spatial distribution is necessary to obtain realistic estimation of inflow quantity to tunnel and underground excavations. In this research, a computer code for three-dimensional discrete fracture network modeling of water inflow into underground excavations was developed. In this code, the fractures are simulated as ellipsoid while geometrical properties of the fractures are reproduced using a stochastic method. Properties such as the size, orientation, and density of the fractures are modeled by their respective probability distributions, which are obtained from field measurements. According to the fracture condition, the flow paths in rock mass are determined. The flow paths are considered as channels with rectangular sections in which channel width and fracture aperture determine geometry of channel section. Inflow into excavation is predicted ignoring matrix permeability and considering the hydrogeological conditions. To verify presented model, simulation results were compared to a part of the Cheshmeh-Roozieh water transfer tunnel in Iran. The results obtained from this research are in good agreement with the field data. Thus, the average of the predicted inflow has just an approximation error equal to 17.8%, and its standard deviation is 8.6 l/s, which is equal to 21% of the observed value that demonstrates low dispersion of the predicted values.
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