Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 2
Abstract
In active learning Kriging (AK) reliability-based analysis, a surrogate model is trained in a stepwise manner and used to evaluate the reliability of the desired system by reducing the computational cost of analysis. While extensive studies were conducted on advancing the AK reliability methods by developing new learning functions, limited work studied the effect of AK configuration on the accuracy, efficiency, and consistency of the AK reliability analysis. AK configuration is defined herein as a unique set of Kriging correlation, Kriging regression, learning function, and AK reliability method for the AK procedure. This paper presents six metric systems to evaluate the performance of AK reliability analysis based on AK configurations including the comprehensive metric system (CMS), the weighted metric system (WMS) with local optimized weights or average optimized weights (LOW or AOW), and modified desirability function, and two original desirability functions used for multiple response optimization. The ranking optimizes four scaled indexes as measures of accuracy, efficiency, and consistency of the reliability analysis. The metrics are developed and applied to four diverse examples, where a total of 14,400 AK reliability analyses were considered. The results show the validity of the metric systems to rank AK configurations based on their performance.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including the numerical values of the figures and input data used for the analysis.
Acknowledgments
The authors would like to thank Mathematics of Information Technology and Complex Systems (MITACS), Norlander Oudah Engineering Ltd (NOEL), and the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting this research program.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 2 • June 2024
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© 2024 American Society of Civil Engineers.
History
Received: Nov 7, 2022
Accepted: Jan 10, 2024
Published online: Mar 28, 2024
Published in print: Jun 1, 2024
Discussion open until: Aug 28, 2024
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