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

By Gang Zheng, Wenbin Zhang, Wengang Zhang, Hai-zuo Zhou, Yang Pengbo. 2020

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Abstract 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 for predicting the liquefaction-induced uplift displacement of tunnels based on artificial databases generated by the finite difference method (FDM). The performance of the SVM and ANN models are assessed using statistical parameters, including the coefficient of determination (R2), the mean absolute error (MAE), and the root mean squared error (RMSE). The applications for the above-mentioned approach for predicting the uplift displacement of tunnels are compared and discussed. A relative importance analysis is adopted to quantify the sensitivity of each input variable. The precision of the presented models is demonstrated using the centrifuge test results available from previous studies.

Subfields: Civil and Structural Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/bd112fa30b503bf48cd2f55bb8d49200532e6ae9