@Article{cmes.2020.010688,
AUTHOR = {Jiaqi He, Yangjun Luo},
TITLE = {A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {2},
PAGES = {777--800},
URL = {http://www.techscience.com/CMES/v125n2/40316},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2020.010688}
}