DAMAGE ASSESSMENT OF BEAMS USING AN ARTIFICIAL NEURAL NETWORK AND NATURAL FREQUENCIES.
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| Title: | DAMAGE ASSESSMENT OF BEAMS USING AN ARTIFICIAL NEURAL NETWORK AND NATURAL FREQUENCIES. |
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| Authors: | Tufisi, Cristian1, Gillich, Gilbert-Rainer1, Popescu, Cristinel2, Ardeljan, Mario1 |
| Source: | Annals of 'Constantin Brancusi' University of Targu-Jiu. Engineering Series / Analele Universităţii Constantin Brâncuşi din Târgu-Jiu. Seria Inginerie. 2021, Issue 2, p44-51. 8p. |
| Subjects: | Artificial neural networks, Feedforward neural networks, Bayesian analysis |
| Abstract: | The current paper presents a modal-based damage identification method that uses a one-layer neural network based on Bayesian regularization to estimate the location and severity of transverse cracks present in cantilever beams. A feedforward neural network is created, and it is trained by employing the relative frequency shift curves (RFS) for known damage positions and depths. The RFS values are plotted by using the squared modal curvature for the first eight weak-axis vibration modes and an enhanced method for assessing the severity for cracks of different depths. Training data were obtained for specific damage positions, by removing the transverse crack with a step of 10 mm along the cantilever beam. The ANN system is trained both to depict the location and severity of transverse cracks present in cantilever beams, and the results obtained are evaluated using data generated from FEM analysis. [ABSTRACT FROM AUTHOR] |
| Copyright of Annals of 'Constantin Brancusi' University of Targu-Jiu. Engineering Series / Analele Universităţii Constantin Brâncuşi din Târgu-Jiu. Seria Inginerie is the property of Universitatea Constantin Brancusi din Targu-Jiu 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 154356054 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DAMAGE ASSESSMENT OF BEAMS USING AN ARTIFICIAL NEURAL NETWORK AND NATURAL FREQUENCIES. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tufisi%2C+Cristian%22">Tufisi, Cristian</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gillich%2C+Gilbert-Rainer%22">Gillich, Gilbert-Rainer</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Popescu%2C+Cristinel%22">Popescu, Cristinel</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ardeljan%2C+Mario%22">Ardeljan, Mario</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annals+of+'Constantin+Brancusi'+University+of+Targu-Jiu%2E+Engineering+Series+%2F+Analele+Universităţii+Constantin+Brâncuşi+din+Târgu-Jiu%2E+Seria+Inginerie%22">Annals of 'Constantin Brancusi' University of Targu-Jiu. Engineering Series / Analele Universităţii Constantin Brâncuşi din Târgu-Jiu. Seria Inginerie</searchLink>. 2021, Issue 2, p44-51. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Feedforward+neural+networks%22">Feedforward neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The current paper presents a modal-based damage identification method that uses a one-layer neural network based on Bayesian regularization to estimate the location and severity of transverse cracks present in cantilever beams. A feedforward neural network is created, and it is trained by employing the relative frequency shift curves (RFS) for known damage positions and depths. The RFS values are plotted by using the squared modal curvature for the first eight weak-axis vibration modes and an enhanced method for assessing the severity for cracks of different depths. Training data were obtained for specific damage positions, by removing the transverse crack with a step of 10 mm along the cantilever beam. The ANN system is trained both to depict the location and severity of transverse cracks present in cantilever beams, and the results obtained are evaluated using data generated from FEM analysis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annals of 'Constantin Brancusi' University of Targu-Jiu. Engineering Series / Analele Universităţii Constantin Brâncuşi din Târgu-Jiu. Seria Inginerie is the property of Universitatea Constantin Brancusi din Targu-Jiu 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 44 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Feedforward neural networks Type: general – SubjectFull: Bayesian analysis Type: general Titles: – TitleFull: DAMAGE ASSESSMENT OF BEAMS USING AN ARTIFICIAL NEURAL NETWORK AND NATURAL FREQUENCIES. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tufisi, Cristian – PersonEntity: Name: NameFull: Gillich, Gilbert-Rainer – PersonEntity: Name: NameFull: Popescu, Cristinel – PersonEntity: Name: NameFull: Ardeljan, Mario IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 18424856 Numbering: – Type: issue Value: 2 Titles: – TitleFull: Annals of 'Constantin Brancusi' University of Targu-Jiu. Engineering Series / Analele Universităţii Constantin Brâncuşi din Târgu-Jiu. Seria Inginerie Type: main |
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