Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation
The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/14/4971 |
| _version_ | 1797445519414394880 |
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| author | Pang Chen Hui Wang Shaojun Cao Xueyuan Lv |
| author_facet | Pang Chen Hui Wang Shaojun Cao Xueyuan Lv |
| author_sort | Pang Chen |
| collection | DOAJ |
| description | The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (<i>D/t</i>) and the tensile strength of the FRP (<i>f</i><sub>frp</sub>) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns. |
| first_indexed | 2024-03-09T13:26:58Z |
| format | Article |
| id | doaj.art-20d9fae50be3496aae3dae36aa737b05 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T13:26:58Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-20d9fae50be3496aae3dae36aa737b052023-11-30T21:22:15ZengMDPI AGMaterials1996-19442022-07-011514497110.3390/ma15144971Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance EvaluationPang Chen0Hui Wang1Shaojun Cao2Xueyuan Lv3School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, ChinaChina Construction First Group Construction & Development Co., Ltd., Beijing 100102, ChinaThe prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (<i>D/t</i>) and the tensile strength of the FRP (<i>f</i><sub>frp</sub>) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.https://www.mdpi.com/1996-1944/15/14/4971FRP-confined circular concrete columnsartificial neural networksupport vector regressionmechanical behaviours |
| spellingShingle | Pang Chen Hui Wang Shaojun Cao Xueyuan Lv Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation Materials FRP-confined circular concrete columns artificial neural network support vector regression mechanical behaviours |
| title | Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation |
| title_full | Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation |
| title_fullStr | Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation |
| title_full_unstemmed | Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation |
| title_short | Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation |
| title_sort | prediction of mechanical behaviours of frp confined circular concrete columns using artificial neural network and support vector regression modelling and performance evaluation |
| topic | FRP-confined circular concrete columns artificial neural network support vector regression mechanical behaviours |
| url | https://www.mdpi.com/1996-1944/15/14/4971 |
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