Topology optimization of truss structures under failure probability using the Bernstein approximation

A novel topology optimization approach for the robust design of structures is presented. The method considers both deterministic and random loadings, and minimizes the compliance subject to a constraint on the volume, as well as a constraint on the failure probability. Handling the failure probabili...

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Published inComputers & structures Vol. 296; p. 107295
Main Authors Canelas, Alfredo, Carrasco, Miguel, López, Julio
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2024
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ISSN0045-7949
1879-2243
DOI10.1016/j.compstruc.2024.107295

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Abstract A novel topology optimization approach for the robust design of structures is presented. The method considers both deterministic and random loadings, and minimizes the compliance subject to a constraint on the volume, as well as a constraint on the failure probability. Handling the failure probability is often challenging in numerical terms, potentially leading to an intractable model as the problem scales. It is addressed by employing the Bernstein approximation, resulting in a model that has the remarkable property of being a linear conic programming problem, therefore, solvable in polynomial time with respect to the input size by using interior point methods. Furthermore, a more efficient reformulation of the problem, involving small semidefinite constraints is derived. To demonstrate the practicality of the proposed method, solutions to several examples of truss topology optimization are provided. •A topology optimization model for design of robust trusses is proposed.•A failure probability constraint in the compliance of the truss is considered.•The failure probability is approximated using the Bernstein inequality.•A tractable convex conic reformulation of the optimization model is obtained.•Numerical examples show that the conic reformulation enables time-saving.
AbstractList A novel topology optimization approach for the robust design of structures is presented. The method considers both deterministic and random loadings, and minimizes the compliance subject to a constraint on the volume, as well as a constraint on the failure probability. Handling the failure probability is often challenging in numerical terms, potentially leading to an intractable model as the problem scales. It is addressed by employing the Bernstein approximation, resulting in a model that has the remarkable property of being a linear conic programming problem, therefore, solvable in polynomial time with respect to the input size by using interior point methods. Furthermore, a more efficient reformulation of the problem, involving small semidefinite constraints is derived. To demonstrate the practicality of the proposed method, solutions to several examples of truss topology optimization are provided. •A topology optimization model for design of robust trusses is proposed.•A failure probability constraint in the compliance of the truss is considered.•The failure probability is approximated using the Bernstein inequality.•A tractable convex conic reformulation of the optimization model is obtained.•Numerical examples show that the conic reformulation enables time-saving.
ArticleNumber 107295
Author Carrasco, Miguel
Canelas, Alfredo
López, Julio
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Keywords Topology optimization
Robust optimization
Reliability design optimization
Conic programming
Bernstein approximation
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  ident: 10.1016/j.compstruc.2024.107295_br0210
  article-title: Chance constrained uncertain classification via robust optimization
  publication-title: Math Program
  doi: 10.1007/s10107-010-0415-1
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Snippet A novel topology optimization approach for the robust design of structures is presented. The method considers both deterministic and random loadings, and...
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elsevier
SourceType Enrichment Source
Index Database
Publisher
StartPage 107295
SubjectTerms Bernstein approximation
Conic programming
Reliability design optimization
Robust optimization
Topology optimization
Title Topology optimization of truss structures under failure probability using the Bernstein approximation
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