A study on static bending behavior of partially elastically supported functionally graded plate with porous voids and prediction of deformation through deep learning.
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| Title: | A study on static bending behavior of partially elastically supported functionally graded plate with porous voids and prediction of deformation through deep learning. |
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| Authors: | Jain, Rajat1 (AUTHOR), Azam, Mohammad Sikandar1 (AUTHOR) mdsazam@gmail.com |
| Source: | Mechanics Based Design of Structures & Machines. 2025, Vol. 53 Issue 1, p245-275. 31p. |
| Subjects: | Artificial neural networks, Rayleigh-Ritz method, Elastic foundations, Deep learning, Porosity |
| Abstract: | This research explores the static bending behaviour of functionally graded rectangular plates with porous voids. It addresses maximum deformation and static bending factors under uniform-pressure, considering variables such as porous-void distributions, full and partial elastic foundations, and edge constraints. The Rayleigh-Ritz method combined with algebraic polynomials is employed to obtain the numerical solutions. The convergence test shows computing efficiency, while the validation tests against public data and ANSYS findings verify the accuracy of the present numerical model. Additionally, this research presents a deep learning-based Artificial-Neural-Network model for deformation prediction to enhance the depth of the analysis without extensive numerical simulations. [ABSTRACT FROM AUTHOR] |
| Copyright of Mechanics Based Design of Structures & Machines is the property of Taylor & Francis Ltd 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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 181862197 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A study on static bending behavior of partially elastically supported functionally graded plate with porous voids and prediction of deformation through deep learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jain%2C+Rajat%22">Jain, Rajat</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Azam%2C+Mohammad+Sikandar%22">Azam, Mohammad Sikandar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mdsazam@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mechanics+Based+Design+of+Structures+%26+Machines%22">Mechanics Based Design of Structures & Machines</searchLink>. 2025, Vol. 53 Issue 1, p245-275. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Rayleigh-Ritz+method%22">Rayleigh-Ritz method</searchLink><br /><searchLink fieldCode="DE" term="%22Elastic+foundations%22">Elastic foundations</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Porosity%22">Porosity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This research explores the static bending behaviour of functionally graded rectangular plates with porous voids. It addresses maximum deformation and static bending factors under uniform-pressure, considering variables such as porous-void distributions, full and partial elastic foundations, and edge constraints. The Rayleigh-Ritz method combined with algebraic polynomials is employed to obtain the numerical solutions. The convergence test shows computing efficiency, while the validation tests against public data and ANSYS findings verify the accuracy of the present numerical model. Additionally, this research presents a deep learning-based Artificial-Neural-Network model for deformation prediction to enhance the depth of the analysis without extensive numerical simulations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mechanics Based Design of Structures & Machines is the property of Taylor & Francis Ltd 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181862197 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15397734.2024.2364894 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 245 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Rayleigh-Ritz method Type: general – SubjectFull: Elastic foundations Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Porosity Type: general Titles: – TitleFull: A study on static bending behavior of partially elastically supported functionally graded plate with porous voids and prediction of deformation through deep learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jain, Rajat – PersonEntity: Name: NameFull: Azam, Mohammad Sikandar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15397734 Numbering: – Type: volume Value: 53 – Type: issue Value: 1 Titles: – TitleFull: Mechanics Based Design of Structures & Machines Type: main |
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