Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing
In recent years, there has been a notable increase in the application of near-surface mounted fiber-reinforced polymer (FRP) reinforcement in reinforced concrete structures. Nevertheless, there is a discernible disparity in the accessibility of accurate and customize measures for augmenting flexural...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Pouyan Press
2024-10-01
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| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_186687_d41d8cd98f00b204e9800998ecf8427e.pdf |
| _version_ | 1797368283077279744 |
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| author | Amin Raeisi Mohammad Kazem Sharbatdar Hosein Naderpour Pouyan Fakharian |
| author_facet | Amin Raeisi Mohammad Kazem Sharbatdar Hosein Naderpour Pouyan Fakharian |
| author_sort | Amin Raeisi |
| collection | DOAJ |
| description | In recent years, there has been a notable increase in the application of near-surface mounted fiber-reinforced polymer (FRP) reinforcement in reinforced concrete structures. Nevertheless, there is a discernible disparity in the accessibility of accurate and customize measures for augmenting flexural strength through the use of near-surface mounted (NSM) reinforcement techniques. Although several basic models have been proposed to predict the flexural capacity achievable with this technology, established codes have not yet provided mathematical equations for this specific purpose. This study presents two separate methodologies with the objective of enhancing the development of suitable code provisions. In the first stage, A comprehensive and reliable database has been developed to leverage the predictive accuracy of neural networks in the computation of the flexural capacity of reinforced beams that utilize near-surface mounted reinforcement. Following this, the results obtained from the neural network are employed to construct a linear equation using the group method of data handling (GMDH) technique. The presented equation has been carefully formulated to produce a concise and simple mathematical expression that enables the determination of the flexural strength of a beam on the field. The evaluation of the accuracy and effectiveness of both the neural network and the suggested equation is conducted in accordance with the requirements specified in ACI 440.R2 for externally bonded reinforcements. The neural network's prediction has a mean absolute error of just 5% in comparison to the experimental results and the GMDH equations exhibit a noteworthy level of concurrence with the experimental outcomes, as they display a mean absolute error of 16%. |
| first_indexed | 2024-03-08T17:30:20Z |
| format | Article |
| id | doaj.art-1257f50112ba4dcdac1dae6371750085 |
| institution | Directory Open Access Journal |
| issn | 2588-2872 |
| language | English |
| last_indexed | 2024-03-08T17:30:20Z |
| publishDate | 2024-10-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj.art-1257f50112ba4dcdac1dae63717500852024-01-02T15:40:07ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722024-10-018412610.22115/scce.2024.429316.1761186687Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft ComputingAmin Raeisi0Mohammad Kazem Sharbatdar1Hosein Naderpour2Pouyan Fakharian3M.Sc. Graduated, Faculty of Civil Engineering, Semnan University, Semnan, IranProfessor, Faculty of Civil Engineering, Semnan University, Semnan, IranProfessor, Faculty of Civil Engineering, Semnan University, Semnan, IranFaculty of Civil Engineering, Semnan University, Semnan, IranIn recent years, there has been a notable increase in the application of near-surface mounted fiber-reinforced polymer (FRP) reinforcement in reinforced concrete structures. Nevertheless, there is a discernible disparity in the accessibility of accurate and customize measures for augmenting flexural strength through the use of near-surface mounted (NSM) reinforcement techniques. Although several basic models have been proposed to predict the flexural capacity achievable with this technology, established codes have not yet provided mathematical equations for this specific purpose. This study presents two separate methodologies with the objective of enhancing the development of suitable code provisions. In the first stage, A comprehensive and reliable database has been developed to leverage the predictive accuracy of neural networks in the computation of the flexural capacity of reinforced beams that utilize near-surface mounted reinforcement. Following this, the results obtained from the neural network are employed to construct a linear equation using the group method of data handling (GMDH) technique. The presented equation has been carefully formulated to produce a concise and simple mathematical expression that enables the determination of the flexural strength of a beam on the field. The evaluation of the accuracy and effectiveness of both the neural network and the suggested equation is conducted in accordance with the requirements specified in ACI 440.R2 for externally bonded reinforcements. The neural network's prediction has a mean absolute error of just 5% in comparison to the experimental results and the GMDH equations exhibit a noteworthy level of concurrence with the experimental outcomes, as they display a mean absolute error of 16%.https://www.jsoftcivil.com/article_186687_d41d8cd98f00b204e9800998ecf8427e.pdfmachine learningartificial neural networkpredictive modelnear-surface mounted (nsm)frpflexural strength |
| spellingShingle | Amin Raeisi Mohammad Kazem Sharbatdar Hosein Naderpour Pouyan Fakharian Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing Journal of Soft Computing in Civil Engineering machine learning artificial neural network predictive model near-surface mounted (nsm) frp flexural strength |
| title | Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing |
| title_full | Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing |
| title_fullStr | Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing |
| title_full_unstemmed | Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing |
| title_short | Flexural Capacity Prediction of RC Beams Strengthened in Terms of NSM System Using Soft Computing |
| title_sort | flexural capacity prediction of rc beams strengthened in terms of nsm system using soft computing |
| topic | machine learning artificial neural network predictive model near-surface mounted (nsm) frp flexural strength |
| url | https://www.jsoftcivil.com/article_186687_d41d8cd98f00b204e9800998ecf8427e.pdf |
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