MULTILAYER PERCEPTION NEURAL NETWORK FOR ASSESSMENT AND PREDICTION OF EARTHQUAKE-INDUCED SAND LIQUEFACTION

Sand can be severely liquefied under the action of earthquakes. The instability of foundation caused by sand liquefaction can cause serious damage to various facilities such as roads, buildings and dams. Therefore, the prediction of sand liquefaction under earthquake action has always been a hot topic in the field of geological disasters. This paper uses 166 sets of identification data of earthquake induced sand liquefaction cases that occurred in the world in the past few decades. Through numerous data training and parameter analysis, we establish the sand liquefaction assessment model based on machine learning. The results show that the network structure is optimized when the network structure is 6(input layer)-15(hidden layer)-1(output layer) and the training function is Levenberg-Marquardt. The best prediction accuracy of test data can reach 96%. The results of parameter analysis show that different parameters have different effects on the accuracy of network prediction. Cone end resistance, the surface normalized peak horizontal acceleration has a relatively large impact. Earthquake magnitude, total vertical stress, effective vertical stress have moderate influence; penetration depth has less effect. Therefore, the input parameters can be simplified appropriately under the conditions of different network prediction accuracy requirements.