Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 399, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2022.115345
Keywords
Structural reliability analysis; Distribution parameter uncertainty; Predictive failure probability; Conditional reliability index; Conditional failure probability; Three -parameter lognormal distribution
Funding
- National Natural Science Foundation of China [51738001]
- China Scholarship Council [202006370005]
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This study proposes an efficient and accurate method to quantify failure probability considering the uncertainty of distribution parameters in structural reliability analysis. The method integrates the probability space of the conditional reliability index to obtain predictive failure probability, providing a complete picture of structural reliability evaluation results.
It has been recognized that the uncertainty of distribution parameters has a significant effect on the outcome of structural reliability analysis. In this study, an efficient and accurate method is proposed to fully quantify failure probability, considering the uncertainty of the distribution parameters. This method obtains predictive failure probability by integrating the probability space of the conditional reliability index instead of directly utilizing the point-estimate method on the conditional failure probability as in previous studies. In the proposed method, a three-parameter lognormal distribution is suggested to approach the distribution of the conditional reliability index. The first three central moments of the conditional reliability index are estimated utilizing the point-estimate method combined with the bivariate dimension-reduction method. Based on this, the analytical solutions for the quantiles and probability distribution of the conditional failure probability can be readily derived without redundant calculations. Four illustrative examples were investigated to verify the efficiency and accuracy of the proposed method. The results show that the proposed method produces sufficiently accurate results in an efficient and simple way. It also presents a complete picture of the structural reliability evaluation results considering distribution parameter uncertainty in a wide range of applications. (c) 2022 Elsevier B.V. All rights reserved.
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