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A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data

Jiaqi He1, Yangjun Luo1,2,*
1 Province Key Laboratory of Advanced Technology for Aerospace Vehicles, School of Aeronautics and Astronautics, Dalian University of Technology, Dalian, 116024, China
2 State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China
* Corresponding Author: Yangjun Luo. Email:
(This article belongs to this Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)

Computer Modeling in Engineering & Sciences 2020, 125(2), 777-800. https://doi.org/10.32604/cmes.2020.010688

Received 20 March 2020; Accepted 17 July 2020; Issue published 12 October 2020

Abstract

For structures that only the predicted bounds of uncertainties are available, this study proposes a Bayesian method to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model and performance test data. According to the given interval ranges of uncertainties, we determine the initial characteristic parameters of a multi-ellipsoid convex set. Moreover, to update the plausibility of characteristic parameters, a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed. Then, an updated multi-ellipsoid set with the maximum likelihood of the performance test data can be achieved. The credible non-probabilistic reliability index is calculated based on the Kriging-based surrogate model of the performance function. Several numerical examples are presented to validate the proposed Bayesian updating method.

Keywords

Convex model; Bayesian method; non-probabilistic reliability; information fusion

Cite This Article

He, J., Luo, Y. (2020). A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data. CMES-Computer Modeling in Engineering & Sciences, 125(2), 777–800.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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