A new hybrid uncertainty optimization method for structures using orthogonal series expansion

Publication Type:
Journal Article
Citation:
Applied Mathematical Modelling, 2017, 45 pp. 474 - 490
Issue Date:
2017-05-01
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1-s2.0-S0307904X17300100-main.pdfAccepted Manuscript Version1.1 MB
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© 2017 Elsevier Inc. This paper proposes a new hybrid uncertain design optimization method for structures which contain both random and interval variables simultaneously. The optimization model is formulated with the feasible robustness and the reliability of the worst scenario. The hybrid uncertainty is quantified by using the orthogonal series expansion method that integrates the Polynomial Chaos (PC) expansion method and the Chebyshev interval method within a uniform framework. The design sensitivity of objective and constraints will be developed to greatly facilitate the use of gradient-based optimization algorithms. The numerical results show that this method will be more possible to seek the feasible solution.
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