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Structural and Multidisciplinary Optimization

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20.11.2017 | REVIEW PAPER

Probability-interval hybrid uncertainty analysis for structures with both aleatory and epistemic uncertainties: a review

verfasst von: C. Jiang, J. Zheng, X. Han

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2018

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Abstract

Traditional structural uncertainty analysis is mainly based on probability models and requires the establishment of accurate parametric probability distribution functions using large numbers of experimental samples. In many actual engineering problems, the probability distributions of some parameters can be established due to sufficient samples available, whereas for some parameters, due to the lack or poor quality of samples, only their variation intervals can be obtained, or their probability distribution types can be determined based on the existing data while some of the distribution parameters such as mean and standard deviation can only be given interval estimations. This thus will constitute an important type of probability-interval hybrid uncertain problem, in which the aleatory and epistemic uncertainties both exist. The probability-interval hybrid uncertainty analysis provides an important mean for reliability analysis and design of many complex structures, and has become one of the research focuses in the field of structural uncertainty analysis over the past decades. This paper reviews the four main research directions in this area, i.e., uncertainty modeling, uncertainty propagation analysis, structural reliability analysis, and reliability-based design optimization. It summarizes the main scientific problems, technical difficulties, and current research status of each direction. Based on the review, this paper also provides an outlook for future research in probability-interval hybrid uncertainty analysis.

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Titel: Probability-interval hybrid uncertainty analysis for structures with both aleatory and epistemic uncertainties: a review
verfasst von: C. Jiang
J. Zheng
X. Han
Publikationsdatum: 20.11.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 6/2018
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-017-1864-4

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