Lateral displacement due to liquefaction(D_(H))is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil.Among all earthquake parameters,the standardized cumulative absolute velocity(CA...Lateral displacement due to liquefaction(D_(H))is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil.Among all earthquake parameters,the standardized cumulative absolute velocity(CAV_(5))exhibits the largest correlation with increasing pore water pressure and liquefaction.Furthermore,the complex effect of fine content(FC)at different values has been studied and demonstrated.Nevertheless,these two contexts have not been entered into empirical and semi-empirical models to predict D_(H)This study bridges this gap by adding CAV_(5)to the data set and developing two artificial neural network(ANN)models.The first model is based on the entire range of the parameters,whereas the second model is based on the samples with FC values that are less than the 28%critical value.The results demonstrate the higher accuracy of the second model that is developed even with less data.Additionally,according to the uncertainties in the geotechnical and earthquake parameters,sensitivity analysis was performed via Monte Carlo simulation(MCS)using the second developed ANN model that exhibited higher accuracy.The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D_(H).展开更多
基金The authors are grateful for the technical and financial support provided by the Scientific Innovation Group for Youths of Sichuan Province(No.2019JDTD0017).
文摘Lateral displacement due to liquefaction(D_(H))is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil.Among all earthquake parameters,the standardized cumulative absolute velocity(CAV_(5))exhibits the largest correlation with increasing pore water pressure and liquefaction.Furthermore,the complex effect of fine content(FC)at different values has been studied and demonstrated.Nevertheless,these two contexts have not been entered into empirical and semi-empirical models to predict D_(H)This study bridges this gap by adding CAV_(5)to the data set and developing two artificial neural network(ANN)models.The first model is based on the entire range of the parameters,whereas the second model is based on the samples with FC values that are less than the 28%critical value.The results demonstrate the higher accuracy of the second model that is developed even with less data.Additionally,according to the uncertainties in the geotechnical and earthquake parameters,sensitivity analysis was performed via Monte Carlo simulation(MCS)using the second developed ANN model that exhibited higher accuracy.The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D_(H).
