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

Underground Space

Wenbin Zhang¹  Pengbo Yang¹  Wengang Zhang²  Haizuo Zhou³  Gang Zheng³ 

[1] Key Laboratory of Coast Civil Structure Safety, Tianjin University, Ministry of Education, Tianjin 300072, China;State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China;School of Civil Engineering, Tianjin University, Tianjin 300072, China;

关键词: Artificial neural network;    Support vector machine;    Liquefaction;    Uplift displacement;   

DOI  :  

来源: DOAJ

【 摘 要 】

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 R2, 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.

【 授权许可】

Unknown