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Adaptive surrogate model with active refinement combining Kriging and a trust region method

B. Gaspar, A.P. Teixeira and C. Guedes Soares

Reliability Engineering and System Safety, 2017, vol. 165, issue C, 277-291

Abstract: The reliability analysis of engineering structural systems with limit state functions defined implicitly by time-consuming numerical models (e.g. finite element analysis structural models) requires the use of efficient solution strategies in order to keep the required computational costs at acceptable levels. In this paper, an adaptive Kriging surrogate model with active refinement is proposed to solve component reliability assessment problems (i.e. involving one single design point) with nonlinear and time-consuming implicit limit state functions with a moderate number of input basic random variables. The proposed model, in the first stage, uses an adaptive Kriging-based trust region method to search for the design point in the standard Gaussian space and predict an initial failure probability based on the first-order reliability method as well as sensitivity factors for the input basic random variables. This initial prediction is then verified or improved efficiently in a second stage using Monte Carlo simulation with importance sampling based on a Kriging surrogate model defined iteratively around the design point using an active refinement algorithm. A convergence criterion that detects the stabilization of the failure probability prediction during the active refinement process is also proposed and implemented. The usefulness of the proposed adaptive Kriging surrogate model in terms of accuracy and efficiency for reliability assessment of engineering structural systems is shown in the paper with two relevant numerical examples, involving a highly nonlinear analytical limit state function in two-dimensions and an advanced nonlinear finite element analysis structural model in a larger dimensional space.

Keywords: Structural reliability analysis; Adaptive surrogate models; Kriging interpolation models; Adaptive trust region; Active refinement algorithm; Monte Carlo simulation; Importance sampling (search for similar items in EconPapers)
Date: 2017
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Handle: RePEc:eee:reensy:v:165:y:2017:i:c:p:277-291