LS-SVM and Monte Carlo methods based reliability analysis for settlement of soft clayey foundation

Abstract

A method which adopts the combination of least squares support vector machine (LS-SVM) and Monte Carlo (MC) simulation is used to calculate the foundation settlement reliability. When using LS-SVM, choosing the training dataset and the values for LS-SVM parameters is the key. In a representative sense, the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset, and the outputs are calculated by using fast Lagrangian analysis continua (FLAC). The decimal ant colony algorithm (DACA) is also used to determine the parameters. Calculation results show that the values of the two parameters, γ and δ2 have great effect on the performance of LS-SVM. After the training of LS-SVM, the inputs are sampled according to the probabilistic distribution, and the outputs are predicted with the trained LS-SVM, thus the reliability analysis can be performed by the MC method. A program compiled by Matlab is employed to calculate its reliability. Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.