Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems

Dutta, S; Gandomi, AH

Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems

Dutta, S Gandomi, AH

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Publisher:: American Society of Civil Engineers (ASCE)
Publication Type:: Journal Article
Citation:: Journal of Structural Engineering (United States), 2020, 146, (11), pp. 04020245-04020245
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Dutta, S
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2020-11-16T20:12:09Z
dc.date.available	2020-11-16T20:12:09Z
dc.date.issued	2020-11-01
dc.identifier.citation	Journal of Structural Engineering (United States), 2020, 146, (11), pp. 04020245-04020245
dc.identifier.issn	0733-9445
dc.identifier.issn	1943-541X
dc.identifier.uri	http://hdl.handle.net/10453/144060
dc.description.abstract	© 2020 American Society of Civil Engineers. Optimization under uncertainty (OUU) is a robust framework to obtain optimal designs for real engineering problems considering uncertainties. The numerical solution for large-scale problems involving millions of degrees-of-freedom is typically computation-intensive in nature. Also, OUU problems constitutes an uncertainty analysis, involving a computation-intensive numerical solver for large-scale systems. Hence, the solution of OUU problems are computationally demanding in nature. In this study, a bilevel data-driven modeling framework is proposed using proper orthogonal decomposition (POD) and polynomial chaos expansion (PCE) metamodels. A heuristic particle swarm optimization (PSO) technique is used for optimization. The effectiveness of the POD-PCE metamodel combined with PSO is demonstrated for two practical large-scale structural optimizations under uncertainty problems. From the case studies, it has been observed that the proposed method gives solutions that are almost hundreds and thousands of times faster as compared to the crude Monte Carlo simulation.
dc.language	en
dc.publisher	American Society of Civil Engineers (ASCE)
dc.relation.ispartof	Journal of Structural Engineering (United States)
dc.relation.isbasedon	10.1061/(ASCE)ST.1943-541X.0002795
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0912 Materials Engineering, 0913 Mechanical Engineering
dc.subject.classification	Civil Engineering
dc.title	Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems
dc.type	Journal Article
utslib.citation.volume	146
utslib.for	0905 Civil Engineering
utslib.for	0912 Materials Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-16T20:12:04Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	146
utslib.citation.issue	11

Abstract:

© 2020 American Society of Civil Engineers. Optimization under uncertainty (OUU) is a robust framework to obtain optimal designs for real engineering problems considering uncertainties. The numerical solution for large-scale problems involving millions of degrees-of-freedom is typically computation-intensive in nature. Also, OUU problems constitutes an uncertainty analysis, involving a computation-intensive numerical solver for large-scale systems. Hence, the solution of OUU problems are computationally demanding in nature. In this study, a bilevel data-driven modeling framework is proposed using proper orthogonal decomposition (POD) and polynomial chaos expansion (PCE) metamodels. A heuristic particle swarm optimization (PSO) technique is used for optimization. The effectiveness of the POD-PCE metamodel combined with PSO is demonstrated for two practical large-scale structural optimizations under uncertainty problems. From the case studies, it has been observed that the proposed method gives solutions that are almost hundreds and thousands of times faster as compared to the crude Monte Carlo simulation.

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http://hdl.handle.net/10453/144060