Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement
Deflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, partic...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/8/3429 |
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| author | Željka Beljkaš Nikola Baša |
| author_facet | Željka Beljkaš Nikola Baša |
| author_sort | Željka Beljkaš |
| collection | DOAJ |
| description | Deflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, particularly when it comes to continuous beams. Machine learning methods can be applied for predicting a deflection level on continuous beams with GFRP (glass fiber-reinforced polymer) reinforcement and loaded with a concentrated load. This paper presents research on using artificial neural networks for deflection estimation and an optimal prediction model choice. It was necessary to first develop a database, in order to train the neural network. The database was formed based on the results of the experimental research on continuous beams with GFRP reinforcement. Using the best trained neural network model, high accuracy was obtained in estimating deflection, expressed over the mean absolute percentage error, 9.0%. This result indicates a high level of reliability in the prediction of deflection with the help of artificial neural networks. |
| first_indexed | 2024-03-10T12:25:15Z |
| format | Article |
| id | doaj.art-92ea7c907fd24b228211ee3daaf56a60 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:25:15Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-92ea7c907fd24b228211ee3daaf56a602023-11-21T15:08:08ZengMDPI AGApplied Sciences2076-34172021-04-01118342910.3390/app11083429Neural Networks—Deflection Prediction of Continuous Beams with GFRP ReinforcementŽeljka Beljkaš0Nikola Baša1Faculty of Civil Engineering, University of Montenegro, 81000 Podgorica, MontenegroFaculty of Civil Engineering, University of Montenegro, 81000 Podgorica, MontenegroDeflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, particularly when it comes to continuous beams. Machine learning methods can be applied for predicting a deflection level on continuous beams with GFRP (glass fiber-reinforced polymer) reinforcement and loaded with a concentrated load. This paper presents research on using artificial neural networks for deflection estimation and an optimal prediction model choice. It was necessary to first develop a database, in order to train the neural network. The database was formed based on the results of the experimental research on continuous beams with GFRP reinforcement. Using the best trained neural network model, high accuracy was obtained in estimating deflection, expressed over the mean absolute percentage error, 9.0%. This result indicates a high level of reliability in the prediction of deflection with the help of artificial neural networks.https://www.mdpi.com/2076-3417/11/8/3429artificial intelligenceneural networkspredictiondeflectioncontinuous beamGFRP bars |
| spellingShingle | Željka Beljkaš Nikola Baša Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement Applied Sciences artificial intelligence neural networks prediction deflection continuous beam GFRP bars |
| title | Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement |
| title_full | Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement |
| title_fullStr | Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement |
| title_full_unstemmed | Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement |
| title_short | Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement |
| title_sort | neural networks deflection prediction of continuous beams with gfrp reinforcement |
| topic | artificial intelligence neural networks prediction deflection continuous beam GFRP bars |
| url | https://www.mdpi.com/2076-3417/11/8/3429 |
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