An Indicator Response Surface-Based Monte Carlo Method for Efficient Component and System Reliability Analysis

Abstract

Reliability analysis methods are commonly used in engineering design, in order to meet reliability and quality measures. An accurate and efficient computational method is presented for reliability analysis of engineering systems at both the component and system levels. The method can easily handle implicit, highly nonlinear limit-state functions, with correlated or non-correlated random variables, which are described by any probabilistic distribution. It is based on a constructed response surface of an indicator function, which determines the “failure” and “safe” regions, according to the performance function. A Monte Carlo simulation (MCS) calculates the probability of failure based on a response surface of the indicator function, instead of the computationally expensive limit-state function. The Cross-Validated Moving Least Squares (CVMLS) method is used to construct the response surface of the indicator function, based on an Optimum Symmetric Latin Hypercube (OSLH) sampling technique. A number of numerical examples highlight the superior accuracy and efficiency of the proposed method over commonly used reliability methods.