Academic Journal
A DNN-Based Surrogate Constitutive Equation for Geometrically Exact Thin-Walled Rod Members.
| Τίτλος: | A DNN-Based Surrogate Constitutive Equation for Geometrically Exact Thin-Walled Rod Members. |
|---|---|
| Συγγραφείς: | Kassab, Marcos Pires, Campello, Eduardo de Morais Barreto, Ibrahimbegovic, Adnan |
| Πηγή: | Computation; Mar2025, Vol. 13 Issue 3, p63, 24p |
| Θεματικοί όροι: | Artificial neural networks, Structural frames, Energy function, Degrees of freedom, Machine learning |
| Περίληψη: | Kinematically exact rod models were a major breakthrough to evaluate complex frame structures undergoing large displacements and the associated buckling modes. However, they are limited to the analysis of global effects, since the underlying kinematical assumptions typically take into account only cross-sectional rigid-body motion and ocasionally torsional warping. For thin-walled members, local effects can be notably important in the overall behavior of the rod. In the present work, high-fidelity simulations using elastic 3D-solid finite elements are employed to provide input data to train a Deep Neural Newtork-(DNN) to act as a surrogate model of the rod's constitutive equation. It is capable of indirectly representing local effects such as web/flange bending and buckling at a stress-resultant level, yet using only usual rod degrees of freedom as inputs, given that it is trained to predict the internal energy as a function of generalized rod strains. A series of theoretical constraints for the surrogate model is elaborated, and a practical case is studied, from data generation to the DNN training. The outcome is a successfully trained model for a particular choice of cross-section and elastic material, that is ready to be employed in a full rod/frame simulation. [ABSTRACT FROM AUTHOR] |
| Copyright of Computation is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20793197&ISBN=&volume=13&issue=3&date=20250301&spage=63&pages=63-86&title=Computation&atitle=A%20DNN-Based%20Surrogate%20Constitutive%20Equation%20for%20Geometrically%20Exact%20Thin-Walled%20Rod%20Members.&aulast=Kassab%2C%20Marcos%20Pires&id=DOI:10.3390/computation13030063 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A DNN-Based Surrogate Constitutive Equation for Geometrically Exact Thin-Walled Rod Members. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kassab%2C+Marcos+Pires%22">Kassab, Marcos Pires</searchLink><br /><searchLink fieldCode="AR" term="%22Campello%2C+Eduardo+de+Morais+Barreto%22">Campello, Eduardo de Morais Barreto</searchLink><br /><searchLink fieldCode="AR" term="%22Ibrahimbegovic%2C+Adnan%22">Ibrahimbegovic, Adnan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Computation; Mar2025, Vol. 13 Issue 3, p63, 24p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+frames%22">Structural frames</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+function%22">Energy function</searchLink><br /><searchLink fieldCode="DE" term="%22Degrees+of+freedom%22">Degrees of freedom</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Kinematically exact rod models were a major breakthrough to evaluate complex frame structures undergoing large displacements and the associated buckling modes. However, they are limited to the analysis of global effects, since the underlying kinematical assumptions typically take into account only cross-sectional rigid-body motion and ocasionally torsional warping. For thin-walled members, local effects can be notably important in the overall behavior of the rod. In the present work, high-fidelity simulations using elastic 3D-solid finite elements are employed to provide input data to train a Deep Neural Newtork-(DNN) to act as a surrogate model of the rod's constitutive equation. It is capable of indirectly representing local effects such as web/flange bending and buckling at a stress-resultant level, yet using only usual rod degrees of freedom as inputs, given that it is trained to predict the internal energy as a function of generalized rod strains. A series of theoretical constraints for the surrogate model is elaborated, and a practical case is studied, from data generation to the DNN training. The outcome is a successfully trained model for a particular choice of cross-section and elastic material, that is ready to be employed in a full rod/frame simulation. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Computation is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/computation13030063 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 63 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Structural frames Type: general – SubjectFull: Energy function Type: general – SubjectFull: Degrees of freedom Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A DNN-Based Surrogate Constitutive Equation for Geometrically Exact Thin-Walled Rod Members. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kassab, Marcos Pires – PersonEntity: Name: NameFull: Campello, Eduardo de Morais Barreto – PersonEntity: Name: NameFull: Ibrahimbegovic, Adnan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20793197 Numbering: – Type: volume Value: 13 – Type: issue Value: 3 Titles: – TitleFull: Computation Type: main |
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