Reliability analysis using adaptive Polynomial-Chaos Kriging and probability density evolution method

Tong Zhou

doi:10.1016/j.ress.2021.108283

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Reliability analysis using adaptive Polynomial-Chaos Kriging and probability density evolution method

2022-04-01

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Tong Zhou

Yongbo Peng *

DOI：10.1016/j.ress.2021.108283

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Abstract

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An efficient reliability method that combines adaptive Polynomial-Chaos Kriging (PC-Kriging) and probability density evolution method (PDEM) is developed, which is abbreviated as the APCK-PDEM. First, according to the relative contributions of different representative points to the failure probability calculated by the PDEM, the notation of region of interest (ROI) is proposed, in which the representative points make critical contributions to the resultant failure probability. Then, three key aspects involved in the proposed APCK-PDEM are addressed: (a) A new learning function called PDEM-oriented expected improvement function (PEIF) is devised to cater for the demand of PDEM on the PC-Kriging accuracy; (b) A pertinent convergence criterion is defined in terms of the bound of failure probability estimated by the APCK-PDEM; (c) Since the true value of the boundary of ROI is unknown in the PEIF, an iterative determination scheme of this metric is performed at each iteration during the adaptive sampling process. Three examples are studied to showcase the performance of APCK-PDEM, and comparisons are made against other existing reliability methods. Numerical analyses and results show that the APCK-PDEM gains satisfactory estimation accuracy and high computational efficiency.

Keywords:

Polynomial-Chaos Kriging

Probability density evolution method

PDEM-oriented expected improvement function

Active learning

Reliability analysis