Estimation of pore water pressure in the body of earth dams during construction with intelligent models

One of the basic measures in managing the stability of earth dams is to accurately estimate the amount of pore water pressure in the body of the dam during and after its construction. In this study, three different models of artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and gene expression programming (GEP) to estimate the pore water pressure in the body of Kabudwal earthen dams at the time of construction have been studied and compared. Five features including fill level, construction time, reservoir level, impounding rate and fill speed have been used during the 4-year statistical period as input of models in 4 piezometers installed in the dam body. The first three features were the most effective inputs according to the cross-correlation function. In this study, the results obtained from artificial neural network (ANN) in two piezometers according to statistical indicators, provided more accurate answers than gene expression programming (GEP) and ANFIS, but in the other two piezometers it was the opposite. Also, ANFIS and GEP models provided more accurate answers in piezometers that had higher data scatter than ANN model. Finally, based on the GEP model, mathematical relationships between input features and output variables were extracted.