Abstract
This study proposed an inverse modelling procedure for evaluating the anisotropic hydraulic conductivity and its variation induced by excavation in fractured rocks by integrating a strain-dependent hydraulic conductivity model. The time-series measurements of both hydraulic head and discharge were used to construct the objective function for improving the reliability, which was solved with a combined method of orthogonal design, transient groundwater flow modelling, artificial neural network and genetic algorithm-based optimization for reducing the computational cost. The proposed methodology proves its effectiveness by successful inverse modelling of the groundwater flow around the underground caverns at the Jinping-I Hydropower Station.
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