Novel Kriging-Based Variance Reduction Sampling Method for Hybrid Reliability Analysis with Small Failure Probability
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 2
Abstract
For hybrid reliability analysis under random and multi-super-ellipsoidal variables (HRA-RM) with small failure probability, a combination of kriging and subset simulation importance sampling (SSIS) was proposed in this paper. Firstly, to quantify epistemic uncertainties more accurately, the super-ellipsoidal model was used to replace interval/ellipsoid ones. Besides, the real performance function was replaced by a kriging metamodel, which can be updated sequentially by selecting candidate samples from the first and last levels of SSIS. Due to the differences between hybrid reliability analysis (HRA) and probability reliability analysis, an expected modified risk function was adopted to obtain the next updated point. Two varying convergence conditions corresponding to the first and last levels of SSIS were employed in this paper to further improve the efficiency. Under the final kriging metamodel, the maximum failure probability of HRA-RM with small failure probability was calculated by the samples in all levels of SSIS. Finally, four validation examples were applied to demonstrate the accuracy and efficiency of the proposed method.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published paper.
Acknowledgments
This research was funded by the National Science and Technology Major Project of China (Grant No. 2017-V-0013-0065).
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 2 • June 2022
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© 2022 American Society of Civil Engineers.
History
Received: Sep 21, 2021
Accepted: Jan 17, 2022
Published online: Mar 23, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 23, 2022
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