Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars
The use of carbon fiber reinforced polymer (CFRP) strands as prestressed reinforcement in prestressed concrete (PC) structures offers an effective solution to the corrosion issues associated with prestressed steel strands. In this study, the flexural behavior of PC beams reinforced with prestressed...
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2024-11-01
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| author | Hai-Tao Wang Xian-Jie Liu Jie Bai Yan Yang Guo-Wen Xu Min-Sheng Chen |
| author_facet | Hai-Tao Wang Xian-Jie Liu Jie Bai Yan Yang Guo-Wen Xu Min-Sheng Chen |
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| description | The use of carbon fiber reinforced polymer (CFRP) strands as prestressed reinforcement in prestressed concrete (PC) structures offers an effective solution to the corrosion issues associated with prestressed steel strands. In this study, the flexural behavior of PC beams reinforced with prestressed CFRP strands and non-prestressed steel rebars was investigated using finite element modeling (FEM) and artificial neural network (ANN) methods. First, three-dimensional nonlinear FE models were developed. The FE results indicated that the predicted failure mode, load-deflection curve, and ultimate load agreed well with the previous test results. Variations in prestress level, concrete strength, and steel reinforcement ratio shifted the failure mode from concrete crushing to CFRP strand fracture. While the ultimate load generally increased with a higher prestressed level, an excessively high prestress level reduced the ultimate load due to premature fracture of CFRP strands. An increase in concrete strength and steel reinforcement ratio also contributed to a rise in the ultimate load. Subsequently, the verified FE models were utilized to create a database for training the back propagation ANN (BP-ANN) model. The ultimate moments of the experimental specimens were predicted using the trained model. The results showed the correlation coefficients for both the training and test datasets were approximately 0.99, and the maximum error between the predicted and test ultimate moments was around 8%, demonstrating that the BP-ANN method is an effective tool for accurately predicting the ultimate capacity of this type of PC beam. |
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| spelling | doaj-art-715bdec9dfd74656a1897fbe558ede0a2025-08-20T02:28:08ZengMDPI AGBuildings2075-53092024-11-011411359210.3390/buildings14113592Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel RebarsHai-Tao Wang0Xian-Jie Liu1Jie Bai2Yan Yang3Guo-Wen Xu4Min-Sheng Chen5College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, ChinaCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210098, ChinaShanghai Engineering Research Center of CFRP Application Technology in Civil Engineering, China Construction Eighth Engineering Division Co., Ltd., Shanghai 200122, ChinaShanghai Engineering Research Center of CFRP Application Technology in Civil Engineering, China Construction Eighth Engineering Division Co., Ltd., Shanghai 200122, ChinaShanghai Engineering Research Center of CFRP Application Technology in Civil Engineering, China Construction Eighth Engineering Division Co., Ltd., Shanghai 200122, ChinaCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210098, ChinaThe use of carbon fiber reinforced polymer (CFRP) strands as prestressed reinforcement in prestressed concrete (PC) structures offers an effective solution to the corrosion issues associated with prestressed steel strands. In this study, the flexural behavior of PC beams reinforced with prestressed CFRP strands and non-prestressed steel rebars was investigated using finite element modeling (FEM) and artificial neural network (ANN) methods. First, three-dimensional nonlinear FE models were developed. The FE results indicated that the predicted failure mode, load-deflection curve, and ultimate load agreed well with the previous test results. Variations in prestress level, concrete strength, and steel reinforcement ratio shifted the failure mode from concrete crushing to CFRP strand fracture. While the ultimate load generally increased with a higher prestressed level, an excessively high prestress level reduced the ultimate load due to premature fracture of CFRP strands. An increase in concrete strength and steel reinforcement ratio also contributed to a rise in the ultimate load. Subsequently, the verified FE models were utilized to create a database for training the back propagation ANN (BP-ANN) model. The ultimate moments of the experimental specimens were predicted using the trained model. The results showed the correlation coefficients for both the training and test datasets were approximately 0.99, and the maximum error between the predicted and test ultimate moments was around 8%, demonstrating that the BP-ANN method is an effective tool for accurately predicting the ultimate capacity of this type of PC beam.https://www.mdpi.com/2075-5309/14/11/3592prestressed concrete beamsCFRP strandsflexural behaviorfinite element modelingartificial neural network |
| spellingShingle | Hai-Tao Wang Xian-Jie Liu Jie Bai Yan Yang Guo-Wen Xu Min-Sheng Chen Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars Buildings prestressed concrete beams CFRP strands flexural behavior finite element modeling artificial neural network |
| title | Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars |
| title_full | Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars |
| title_fullStr | Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars |
| title_full_unstemmed | Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars |
| title_short | Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars |
| title_sort | finite element modeling and artificial neural network analyses on the flexural capacity of concrete t beams reinforced with prestressed carbon fiber reinforced polymer strands and non prestressed steel rebars |
| topic | prestressed concrete beams CFRP strands flexural behavior finite element modeling artificial neural network |
| url | https://www.mdpi.com/2075-5309/14/11/3592 |
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