Adaptive hyperball Kriging method for efficient reliability analysis. (8th November 2022)

• Record Type: Journal Article
• Authors: Yang, I-Tung; Prayogo, Handy
• Is Part Of: AI EDAM. Volume 36(2022)
• Publication Date: 2022-11-08
• Subjects: Active learning -- Kriging -- reliability analysis -- resilient infrastructure -- soft computing -- structural design -- surrogate modeling; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060422000208
• Languages: English
• ISSNs: 0890-0604

Abstract

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.