Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams
The shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams is often given a large safety margin by current construction requirements. Six characteristics are utilized as inputs to compute the shear strength of FRP-reinforced concrete beams. This study uses 198 samples from the li...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/4062587 |
| _version_ | 1797869974919839744 |
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| author | Mohammad Nikoo Babak Aminnejad Alireza Lork |
| author_facet | Mohammad Nikoo Babak Aminnejad Alireza Lork |
| author_sort | Mohammad Nikoo |
| collection | DOAJ |
| description | The shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams is often given a large safety margin by current construction requirements. Six characteristics are utilized as inputs to compute the shear strength of FRP-reinforced concrete beams. This study uses 198 samples from the literature to predict the shear strength of 139 training samples and 59 testing samples. Additionally, the ANN structure is optimized with the firefly algorithm. The FA-ANN model is also compared to ACI-440, CSA-S806, and BISE-99 codes, and the optimized model by Nehdi et al. Findings show that regarding the shear strength of FRP-reinforced concrete beams, the firefly algorithm-optimized model performs better than the other four models. Concerning accuracy, the coefficient of correlation, R2, was calculated as 0.961, while the average absolute error (AAE) is 0.22 for the shear strength of FRP-reinforced beams. |
| first_indexed | 2024-04-10T00:21:00Z |
| format | Article |
| id | doaj.art-6b349e5de1ad4314a2d3ca10d15372ea |
| institution | Directory Open Access Journal |
| issn | 1687-8094 |
| language | English |
| last_indexed | 2024-04-10T00:21:00Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj.art-6b349e5de1ad4314a2d3ca10d15372ea2023-03-16T00:01:37ZengHindawi LimitedAdvances in Civil Engineering1687-80942023-01-01202310.1155/2023/4062587Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete BeamsMohammad Nikoo0Babak Aminnejad1Alireza Lork2Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringThe shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams is often given a large safety margin by current construction requirements. Six characteristics are utilized as inputs to compute the shear strength of FRP-reinforced concrete beams. This study uses 198 samples from the literature to predict the shear strength of 139 training samples and 59 testing samples. Additionally, the ANN structure is optimized with the firefly algorithm. The FA-ANN model is also compared to ACI-440, CSA-S806, and BISE-99 codes, and the optimized model by Nehdi et al. Findings show that regarding the shear strength of FRP-reinforced concrete beams, the firefly algorithm-optimized model performs better than the other four models. Concerning accuracy, the coefficient of correlation, R2, was calculated as 0.961, while the average absolute error (AAE) is 0.22 for the shear strength of FRP-reinforced beams.http://dx.doi.org/10.1155/2023/4062587 |
| spellingShingle | Mohammad Nikoo Babak Aminnejad Alireza Lork Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams Advances in Civil Engineering |
| title | Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams |
| title_full | Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams |
| title_fullStr | Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams |
| title_full_unstemmed | Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams |
| title_short | Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams |
| title_sort | firefly algorithm based artificial neural network to predict the shear strength in frp reinforced concrete beams |
| url | http://dx.doi.org/10.1155/2023/4062587 |
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