Surrogate-based Reliability Analysis Using Support Vector Machine

An approach of surrogate-based reliability analysis by support vector machine with Monte-Carlo simulation is proposed. The efficient sampling techniques, such as uniform design and Latin Hypercube sampling, are used, and the SVM is trained with the sample pairs of input and output data obtained by the finite element analysis. The trained SVM model, as a solver-surrogate model, is intended to approximate the real performance function. Considering the selection of parameters for SVM affects the learning performance of SVM strongly, the Genetic Algorithm (GA) is integrated to the construction of the SVM, by optimizing the relevant parameters. The influence of the parameters on SVM is discussed and a methodology is proposed for selecting the SVM model. Support Vector Classification (SVC) based and Support Vector Regression (SVR) based reliability analyses are studied. Some numerical examples demonstrate the efficiency and applicability of the proposed method.