Prediction of liquefaction-induced lateral spreading based on Neural network

Abstract

In light of inherent errors associated with the existing methods for predicting lateral spreading of liquefied soil during earthquakes, a novel approach has been proposed. Based on the Newmark sliding block method, a neural network model has been trained to calculate lateral liquefaction displacement, which was achieved by compiling a substantial dataset and establishing a comprehensive seismic motion database. Taking into consideration six input features to train the sensitivity model, based on the sensitivity analysis, a predictive model for liquefaction-induced lateral spreading was developed include three parameters, moment magnitude, peak ground acceleration and yield acceleration. This model was then compared to empirical lateral spreading prediction models. The results demonstrate that this model shows notable concurrence with the existing empirical models. Additionally, using 22 well-documented cases of liquefaction-induced lateral spreading, three high-quality models were employed to predict residual shear strength of the soil. Notably, this novel model surpasses the performance of empirical liquefaction-induced lateral spreading prediction models.