Neural network and support vector machine models for the prediction of the liquefaction-induced uplift displacement of tunnels

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.

Automated summary

This paper introduces SVM and ANN models for predicting liquefaction-induced uplift displacement of tunnels, evaluates their performance using statistical parameters, and compares their applications. The sensitivity of input variables is quantified using relative importance analysis, and the precision of the models is demonstrated with centrifuge test results from previous studies.