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Rare failure event analysis of structures under mixed uncertainties

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Authors
Wei, Pengfei; Bi, Sifeng; Zhang, Yi; Beer, Michael
Issue Date
2019-05-26
Citation
13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019
Abstract
Two challenges may exist in the reliability analysis of highly reliable structures in, e.g., aerospace engineering. The first one is that, the failure probability may be extremely small (typically, smaller than 1e-6), which commonly prevents us from generating accurate estimation with acceptable computational costs by using the available methods. The second one is that, the available information for the input variables may be subject to incompleteness (e.g., sparse data) and/or imprecision (e.g., measuring error), which, makes it impossible to generate precise probability models for the input variables. To address the above two challenges, this work proposes two effective algorithms based on combining the sampling techniques (i.e., extended Monte Carlo simulation and subset simulation), active learning techniques and high-dimensional model representation decomposition. The proposed methods can effectively estimate the failure probability function w.r.t. the uncertain distribution parameters of the input variables with small number of training samples even when the failure event is extremely rare. A numerical test example is introduced to illustrate the proposed methods.
Language
English
URI
https://hdl.handle.net/10371/153270
DOI
https://doi.org/10.22725/ICASP13.040
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)ICASP13
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