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Adaptive hyperball Kriging method for efficient reliability analysis

Published online by Cambridge University Press:  08 November 2022

I-Tung Yang Open the ORCID record for I-Tung Yang [Opens in a new window]  and
Handy Prayogo Open the ORCID record for Handy Prayogo [Opens in a new window]
I-Tung Yang*
Affiliation:
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, No. 43 Section 4 Keelung Road, Taipei, Taiwan
Handy Prayogo
Affiliation:
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, No. 43 Section 4 Keelung Road, Taipei, Taiwan
*
Author for correspondence: I-Tung Yang, E-mail: ityang@mail.ntust.edu.tw
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Abstract

Although an accurate reliability assessment is essential to build a resilient infrastructure, it usually requires time-consuming computation. To reduce the computational burden, machine learning-based surrogate models have been used extensively to predict the probability of failure for structural designs. Nevertheless, the surrogate model still needs to compute and assess a certain number of training samples to achieve sufficient prediction accuracy. This paper proposes a new surrogate method for reliability analysis called Adaptive Hyperball Kriging Reliability Analysis (AHKRA). The AHKRA method revolves around using a hyperball-based sampling region. The radius of the hyperball represents the precision of reliability analysis. It is iteratively adjusted based on the number of samples required to evaluate the probability of failure with a target coefficient of variation. AHKRA adopts samples in a hyperball instead of an n-sigma rule-based sampling region to avoid the curse of dimensionality. The application of AHKRA in ten mathematical and two practical cases verifies its accuracy, efficiency, and robustness as it outperforms previous Kriging-based methods.

Keywords

Active learningKrigingreliability analysisresilient infrastructuresoft computingstructural designsurrogate modeling

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Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e34
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Copyright © The Author(s), 2022. Published by Cambridge University Press

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