Studying Seepage in a Body of Earth-Fill Dam by (artifical Neural Networks) Anns

Abstract

Dams are structures that are used especially for water storage , energy production, and irrigation. Dams are mainly divided into four parts on the basis of the type and materials of construction as gravity dams, buttress dams, arch dams, and embankment dams. There are two types of embankment dams: earthfill dams and rockfill dams. In this study, seepage through an earthfill dam's body is investigated using an artificial neural network model. Seepage is investigated since seepage both in the dam's body and under the foundation adversely affects dam's stability. This study specifically investigated seepage in dam.s body. The seepage in the dams body follows a phreatic line. In order to understand the degree of seepage, it is necessary to measure the level of phreatic line. This measurement is called as piezometric measurement. Piezometric data sets which are collected from Jeziorsko earthfill dam in Poland were used for training and testing the developed ANN model. Jeziorsko dam is a non-homogeneous earthfill dam built on the impervious foundation. Artificial Neural Networks are one of the artificial intelligence related technologies and have many properties. In this study the water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the artificial neural network model. In the line of the purpose of this research, the locus of the seepage path in an earthfill dam is estimated by artificial neural networks. MATLAB 6 neural network toolbox is used for this study.