A Novel Structural Reliability Method on the Basis of Gaussian Mixture and Scaled Unscented Transformation
Publication: Journal of Engineering Mechanics
Volume 147, Issue 12
Abstract
This paper proposes a novel method to capture efficiently the probability distribution of the limit state function (LSF) for structural reliability analysis. This method works by decomposing the probability distribution of the LSF into a Gaussian mixture and applying the scaled unscented transformation to approximate the required statistics for each mixture component. The number of mixture components is specified according to Akaike’s information criterion. Because the required sample size grows linearly with the number of random inputs, little computational effort is required to recover the entire distribution of the LSF with precision. The effectiveness of the proposed method was validated through numerical examples, in which the results obtained from pertinent Monte Carlo simulations and other approaches are compared. The results demonstrated that the proposed method efficiently can yield accurate estimates of the probability distribution of the LSF in the entire range, and the probability of failure accordingly can be evaluated straightforwardly. Issues that need to be studied further were discussed.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The National Natural Science Foundation of China (Grant No. 51978253) and the Fundamental Research Funds for the Central Universities (Grant No. 531107040224) are gratefully appreciated for support of the research.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 24, 2020
Accepted: Aug 12, 2021
Published online: Sep 30, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 28, 2022
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