LAUSR

Sheet piles are of general water retaining structures. The discharge flow rate beneath sheet plies is an important parameter in the design of these structures. In this study, the Gene Expression Programming (GEP) as an Artificial Intelligence (AI) method is used for developing a model to predict the discharge flow rate. The input parameters include the sheet pile height, upstream head and hydraulic conductivity anisotropy ratio. In order to achieve better performance, the flow rate is normalized and selected as an output of the model. A database including 1000 cases are created from the Scaled Boundary Finite Element Method (SBFEM) for the seepage beneath sheet plies is employed to develop the model. The GEP-based model predictions demonstrate a reasonable agreement with the simulated data, which indicates the efficiency of the developed model. The results of the sensitivity analysis indicate that the upstream head is the most influential parameter in the discharge flow rate beneath the sheet piles. Furthermore, the outputs of the parametric analysis show the reasonable performance of the model in the prediction of normalized discharge flow rate.