Prediction of natural frequency of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using artificial neural networks (ANNs) method.
Saved in:
| Title: | Prediction of natural frequency of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using artificial neural networks (ANNs) method. |
|---|---|
| Authors: | Altabey, Wael A.1,2 wael.altabey@gmail.com |
| Source: | Journal of Vibroengineering. Aug2017, Vol. 19 Issue 5, p3668-3678. 11p. 2 Diagrams, 4 Charts, 8 Graphs. |
| Subjects: | Frequencies of oscillating systems, Basalt analysis, Fiber-reinforced plastics, Artificial neural networks, Reinforced plastics, Finite strip method, Fatigue (Physiology) |
| Abstract: | The paper is focused on the application of artificial neural networks (ANNs) in predicting the natural frequency of basalt fiber reinforced polymer (FRP) laminated, variable thickness plates. The author has found that the finite strip transition matrix (FSTM) approach is very effective to study the changes of plate natural frequencies due to intermediate elastic support (IES), but the method difficulty in terms of, a lot of calculations with large number of iterations is the main drawback of the method. For training and testing of the ANN model, a number of FSTM results for different classical boundary conditions (CBCs) with different values of elastic restraint coefficients (KT) for IES have been carried out to training and testing an ANN model. The ANN model has been developed using multilayer perceptron (MLP) Feed-forward neural networks (FFNN). The adequacy of the developed model is verified by the regression coefficient (R2) and Mean Square error (MSE) It was found that the R2 and MSE values are 0.986 and 0.0134 for train and 0.9966 and 0.0122 for test data respectively. The results showed that, the training algorithm of FFNN was sufficient enough in predicting the natural frequency in basalt FRP laminated, variable thickness plates with IES. To judge the ability and efficiency of the developed ANN model, MSE has been used. The results predicted by ANN are in very good agreement with the FSTM results. Consequently, the ANN is show to be effective in predicting the natural frequency of laminated composite plates. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Vibroengineering is the property of Extrica 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 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 124642850 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Prediction of natural frequency of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using artificial neural networks (ANNs) method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Altabey%2C+Wael+A%2E%22">Altabey, Wael A.</searchLink><relatesTo>1,2</relatesTo><i> wael.altabey@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Vibroengineering%22">Journal of Vibroengineering</searchLink>. Aug2017, Vol. 19 Issue 5, p3668-3678. 11p. 2 Diagrams, 4 Charts, 8 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Frequencies+of+oscillating+systems%22">Frequencies of oscillating systems</searchLink><br /><searchLink fieldCode="DE" term="%22Basalt+analysis%22">Basalt analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Fiber-reinforced+plastics%22">Fiber-reinforced plastics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforced+plastics%22">Reinforced plastics</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+strip+method%22">Finite strip method</searchLink><br /><searchLink fieldCode="DE" term="%22Fatigue+%28Physiology%29%22">Fatigue (Physiology)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The paper is focused on the application of artificial neural networks (ANNs) in predicting the natural frequency of basalt fiber reinforced polymer (FRP) laminated, variable thickness plates. The author has found that the finite strip transition matrix (FSTM) approach is very effective to study the changes of plate natural frequencies due to intermediate elastic support (IES), but the method difficulty in terms of, a lot of calculations with large number of iterations is the main drawback of the method. For training and testing of the ANN model, a number of FSTM results for different classical boundary conditions (CBCs) with different values of elastic restraint coefficients (KT) for IES have been carried out to training and testing an ANN model. The ANN model has been developed using multilayer perceptron (MLP) Feed-forward neural networks (FFNN). The adequacy of the developed model is verified by the regression coefficient (R2) and Mean Square error (MSE) It was found that the R2 and MSE values are 0.986 and 0.0134 for train and 0.9966 and 0.0122 for test data respectively. The results showed that, the training algorithm of FFNN was sufficient enough in predicting the natural frequency in basalt FRP laminated, variable thickness plates with IES. To judge the ability and efficiency of the developed ANN model, MSE has been used. The results predicted by ANN are in very good agreement with the FSTM results. Consequently, the ANN is show to be effective in predicting the natural frequency of laminated composite plates. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Vibroengineering is the property of Extrica 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=124642850 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.21595/jve.2017.18209 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 3668 Subjects: – SubjectFull: Frequencies of oscillating systems Type: general – SubjectFull: Basalt analysis Type: general – SubjectFull: Fiber-reinforced plastics Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Reinforced plastics Type: general – SubjectFull: Finite strip method Type: general – SubjectFull: Fatigue (Physiology) Type: general Titles: – TitleFull: Prediction of natural frequency of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using artificial neural networks (ANNs) method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Altabey, Wael A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 13928716 Numbering: – Type: volume Value: 19 – Type: issue Value: 5 Titles: – TitleFull: Journal of Vibroengineering Type: main |
| ResultId | 1 |
