@Article{cmes.2020.010688, AUTHOR = {Jiaqi He, Yangjun Luo,2}, 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} }