Least Squares Support Vector Machine for Constitutive Modeling of Clay

Abstract

Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of SVM in large scale function approximation problems is limited during optimization. In this paper, least squares support vector machine (LSSVM) is proposed to simulate stress-strain relationship of clay. LSSVM is a robust type of SVM, maintains the good features of SVM and also has its own unique advantages. LSSVM offers an effective alternative for mimicking constitutive modeling of clay. The good performance of the LSSVM models is demonstrated by learning and prediction of constitutive relationship of Fujinomori clay under undrained and drained conditions. In the present study, three versions of LSSVM models are built by considering more history points. The results prove that the LSSVM based models are superior to Modified Cam-clay model.