This paper discusses and presents the cumulative absolute velocity （CAV） parameters of the Wenchuan earthquake. Additionally, the CAV calculated from recorded data for the earthquake is compared to the peak ground a...This paper discusses and presents the cumulative absolute velocity （CAV） parameters of the Wenchuan earthquake. Additionally, the CAV calculated from recorded data for the earthquake is compared to the peak ground acceleration（PGA）, based on a brief analysis of background information. Accordingly, the paper studied the relationship between the CAV and PGA, and 3 CAV/PGA ratio charts were obtained in three different sub-directions. Linear and polynomial fitting operations were then used to analyze the potential discipline and characteristics in these directions. Finally, in the study, we investigated the applicability of using the CAV parameter for earthquake observation systems, and the CAV parameter was paired with the currently used PGA to provide earthquake observers and emergency responders with a theoretical basis.展开更多
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supported by the fund of the director,China Earthquake Administration,Research cumulative absolute velocity(CAV)earthquake observation records system
文摘This paper discusses and presents the cumulative absolute velocity （CAV） parameters of the Wenchuan earthquake. Additionally, the CAV calculated from recorded data for the earthquake is compared to the peak ground acceleration（PGA）, based on a brief analysis of background information. Accordingly, the paper studied the relationship between the CAV and PGA, and 3 CAV/PGA ratio charts were obtained in three different sub-directions. Linear and polynomial fitting operations were then used to analyze the potential discipline and characteristics in these directions. Finally, in the study, we investigated the applicability of using the CAV parameter for earthquake observation systems, and the CAV parameter was paired with the currently used PGA to provide earthquake observers and emergency responders with a theoretical basis.
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).
