Tunnels buried in liquefiable deposits are vulnerable to liquefaction-induced uplift damage during earthquakes.This paper presents support vector machine(SVM)and artificial neural network(ANN)models to predict the liq...Tunnels buried in liquefiable deposits are vulnerable to liquefaction-induced uplift damage during earthquakes.This paper presents support vector machine(SVM)and artificial neural network(ANN)models to predict the liquefaction-induced uplift displacement of tunnels based on artificial databases generated by the finite difference method.The performance of the SVM and ANN models was assessed using statistical parameters,including the coefficient of determination R^(2),the mean absolute error,and the root mean squared error.Applications for the above-mentioned approaches are compared and discussed.A relative importance analysis was adopted to quantify the sensitivity of each input variable.The precision of the presented models is demonstrated using centrifuge test results from previous studies.展开更多
基金funded by the National Natural Science Foundation of China(Nos.51708405 and 41630641)the Systematic Project of Guangxi Key Laboratory of Disaster Prevention and Structural Safety(No.2019ZDK031).
文摘Tunnels buried in liquefiable deposits are vulnerable to liquefaction-induced uplift damage during earthquakes.This paper presents support vector machine(SVM)and artificial neural network(ANN)models to predict the liquefaction-induced uplift displacement of tunnels based on artificial databases generated by the finite difference method.The performance of the SVM and ANN models was assessed using statistical parameters,including the coefficient of determination R^(2),the mean absolute error,and the root mean squared error.Applications for the above-mentioned approaches are compared and discussed.A relative importance analysis was adopted to quantify the sensitivity of each input variable.The precision of the presented models is demonstrated using centrifuge test results from previous studies.
