A neural network model for liquefaction-induced horizontal ground displacement

Abstract The horizontal ground displacement generated by seismically induced liquefaction is known to produce significant damage to engineered structures. A backpropagation neural network model is developed to predict the horizontal ground displacements. A large database containing the case histories of lateral spreads observed in eight major earthquakes is used. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the amount of horizontal ground displacement. As more data become available, the model itself can be improved to make more accurate displacement prediction for a wider range of earthquake and site conditions.

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