Abstract:In this study, we add the cumulative absolute velocity (CAV5) to the existing seismic liquefaction-induced lateral spread database to consider the effect of focal mechanism on liquefaction-induced lateral spread. Then, we use the radial basis function neural network (RBFNN) method to establish the liquefaction-induced lateral spread prediction model of earthquake liquefaction. The results show that our model has higher prediction accuracy than other models; CAV5 can replace the magnitude and epicentral distance in the prediction of liquefaction-induced lateral spread. The magnitude, epicentral distance and liquefiable soil layer thickness of all parameters are more sensitive and have a greater impact on liquefaction-induced lateral spread.