Structural system reliability and sensitivity analysis based on support vector machine regression

Based on the support vector machine regression approximating the limit state function, a procedure is presented to analyze the reliability and corresponding sensitivity of the structural system. In the presented procedure, the complicated or implicit limit state functions of the system failure modes are firstly fitted by the support vector machine regression, then according to the system logicality, the methods for the system with explicit limit state function can be employed to estimate the reliability and corresponding sensitivity. Compared with the linear expansion polynomial and the response surface method, the support vector machine regression can match the limit state function better due to its underlying structural risk minimization inference rule, the support vector machine regression is more accurate therefore. Since the presented procedure constructs the surrogate of the implicit or the complicated limit state function by a small quantity of samples, its efficiency is much higher than that of Monte Carlo simulation. The presented procedure can treat the reliability and corresponding sensitivity analysis of the series, parallel and mixed systems, and it has wide applicability in engineering problem, which have been demonstrated by two engineering examples.