Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning
To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/11/1908 |
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| author | Jitao Zhao Miaomiao Zhu Lidan Xu Ming Chen Mingfang Shi |
| author_facet | Jitao Zhao Miaomiao Zhu Lidan Xu Ming Chen Mingfang Shi |
| author_sort | Jitao Zhao |
| collection | DOAJ |
| description | To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). First, the correlation between input and output parameters was analyzed by the Pearson correlation coefficient method. Then, representative single model (ANN) and integrated model (XGBoost) algorithms were selected to predict the dataset, and their performance was evaluated based on three commonly used regression evaluation metrics. Finally, the prediction accuracy of the ML model was further verified by comparing it with the domestic and foreign design codes. The results manifest that the shear capacity exhibits a strong positive correlation with the beam width and effective height. Compared to the ANN model, the XGBoost-based prediction model achieves determination coefficients (R<sup>2</sup>) of 0.999 and 0.879 for the training and test sets, respectively, indicating superior predictive accuracy. Furthermore, the shear capacity calculations from design codes show significant variability, demonstrating the superior predictive capability of ML algorithms. These findings offer a guideline for the design and implementation of FRP reinforcement in actual bridge engineering. |
| format | Article |
| id | doaj-art-fb59c5ba976a47bbbf8b2d2fb83b7005 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-fb59c5ba976a47bbbf8b2d2fb83b70052025-08-20T02:33:06ZengMDPI AGBuildings2075-53092025-06-011511190810.3390/buildings15111908Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine LearningJitao Zhao0Miaomiao Zhu1Lidan Xu2Ming Chen3Mingfang Shi4School of Civil and Architecture Engineering, Panzhihua University, Panzhihua 617000, ChinaSchool of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaSchool of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaSchool of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaSchool of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaTo address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). First, the correlation between input and output parameters was analyzed by the Pearson correlation coefficient method. Then, representative single model (ANN) and integrated model (XGBoost) algorithms were selected to predict the dataset, and their performance was evaluated based on three commonly used regression evaluation metrics. Finally, the prediction accuracy of the ML model was further verified by comparing it with the domestic and foreign design codes. The results manifest that the shear capacity exhibits a strong positive correlation with the beam width and effective height. Compared to the ANN model, the XGBoost-based prediction model achieves determination coefficients (R<sup>2</sup>) of 0.999 and 0.879 for the training and test sets, respectively, indicating superior predictive accuracy. Furthermore, the shear capacity calculations from design codes show significant variability, demonstrating the superior predictive capability of ML algorithms. These findings offer a guideline for the design and implementation of FRP reinforcement in actual bridge engineering.https://www.mdpi.com/2075-5309/15/11/1908FRP reinforcementreinforced concrete beamscapacity predictionmachine learningANNXGBoost |
| spellingShingle | Jitao Zhao Miaomiao Zhu Lidan Xu Ming Chen Mingfang Shi Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning Buildings FRP reinforcement reinforced concrete beams capacity prediction machine learning ANN XGBoost |
| title | Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning |
| title_full | Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning |
| title_fullStr | Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning |
| title_full_unstemmed | Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning |
| title_short | Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning |
| title_sort | prediction of shear capacity of fiber reinforced polymer reinforced concrete beams based on machine learning |
| topic | FRP reinforcement reinforced concrete beams capacity prediction machine learning ANN XGBoost |
| url | https://www.mdpi.com/2075-5309/15/11/1908 |
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