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Optimization-Based Approaches to Uncertainty Analysis of Structures Using Non-Probabilistic Modeling: A Review

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Pubblicato in:Computer Modeling in Engineering & Sciences vol. 143, no. 1 (2025), p. 115
Autore principale: Kanno, Yoshihiro
Altri autori: Takewaki, Izuru
Pubblicazione:
Tech Science Press
Soggetti:
Probability theory
Probabilistic models
Semidefinite programming
Mixed integer
Uncertainty analysis
Integer programming
Machine learning
Parameter uncertainty
Convexity
Mathematical programming
Nonlinear programming
Heuristic methods
Optimization
Design optimization
Fuzzy sets
Random variables
Design specifications
Algorithms
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Abstract:Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization. This paper provides a review on optimization-based methods for uncertainty analysis, with focusing attention on specific properties of adopted numerical optimization approaches. We collect and discuss the methods based on nonlinear programming, semidefinite programming, mixed-integer programming, mathematical programming with complementarity constraints, difference-of-convex programming, optimization methods using surrogate models and machine learning techniques, and metaheuristics. As a closely related topic, we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling. We conclude the paper by drawing several remarks through this review.
ISSN:1526-1492
1526-1506
DOI:10.32604/cmes.2025.061551
Fonte:Advanced Technologies & Aerospace Database