Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran

Abstract

Piezometric heads in the core of Sattarkhan earthfill dam in Iran have been analyzed in this paper via Artificial Neural Network (ANN). Single and integrated ANN models were trained and verified using each piezometer’s data, and also the water levels on the up and downstream of the dam. Therefore, in the single ANN modeling a single ANN was developed for each piezometer, whereas in the integrated ANN modeling only a unique ANN was trained for all piezometers at different cross sections of the dam. Three-layered Perceptron ANN trained with Back Propagation Levenberg-Marquardt scheme was employed in the single modeling; while, two different ANN algorithms, the feed-forward back-propagation (FFBP) and the radial basis function (RBF) were employed to develop integrated ANNs. The number of hidden neurons were determined 5 and 7 for single ANNs, whereas 6 hidden neurons for the integrated FFBP ANN, and the spread value of 0.5 for the integrated RBF. The results show good agreement between computed and observed water heads at different monitoring piezometers with validation determination coefficients higher than 0.7984 in the single and 0.87 and 0.67 in the FFBP and RBF integrated modeling, respectively. Thereafter, the results of the ANNs were satisfactorily compared with the results of a physically based model (Finite Element Model, FEM).