An efficient Monte Carlo reliability assessment methodology is presented for engineering systems with multiple failure regions and potentially multiple most probable points. The method can handle implicit, nonlinear limit-state functions, with correlated or non-correlated random variables, which can be described by any probabilistic distribution. It uses a combination of approximate or “accurate-on-demand,” global and local metamodels which serve as indicators to determine the failure and safe regions. Samples close to limit states define transition regions between safe and failure domains. A clustering technique identifies all transition regions which can be in general disjoint, and local metamodels of the actual limit states are generated for each transition region. A Monte Carlo simulation calculates the probability of failure using the global and local metamodels. A robust maximin “space-filling” sampling technique is used to construct the metamodels. Also, a principal component analysis addresses the problem dimensionality making therefore, the proposed method attractive for problems with a large number of random variables. Two numerical examples highlight the accuracy and efficiency of the method.

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