Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

Abstract

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

Seismic Behavior of Earth Dams with Different Reservoir Water Levels Under Near-Field and Far-Field Earthquakes

“…The behaviour of the San Fernando embankments under seismic loading has been investigated. The analysis involved the importance of investigating a dynamic analysis and emphasizing acceleration reinforcement through the embankment and attempting to communicate with the rigidity of the soil [18]. On the behaviour of earth dam under seismic load, studies have been carried out.…”