Support vector machines in reliability calculations of engineering structures

Abstract

In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.