Abstract
This work addresses the issue of model selection in adaptive kriging-based Monte Carlo reliability analysis. It is shown that arbitrary model selection (kriging trend and correlation) can lead to poor probability of failure estimates for complex systems. We propose a method for kriging model development that employs information-theoretic multimodel inference and introduces an averaged kriging model derived from the associated model probabilities. The proposed multimodel kriging model is then integrated into an adaptive sample selection method that merges the surrogate enhanced stochastic search method with a learning function modified from the adaptive kriging—Monte Carlo simulation (AK-MCS) method. The result is an efficient method for a surrogate model–based reliability analysis that converges as fast as, or faster than, the AK-MCS method but with significantly improved robustness providing greater assurance in model accuracy.
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 5 • Issue 2 • June 2019
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©2019 American Society of Civil Engineers.
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Received: Mar 1, 2018
Accepted: Oct 11, 2018
Published online: Jan 23, 2019
Published in print: Jun 1, 2019
Discussion open until: Jun 23, 2019
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