Compressive Strength Estimation for the Fiber-Reinforced Polymer （FRP）-Confined Concrete Columns with Different Shapes Using Artificial Neural Networks
An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer（ FRP） was presented with extensive collection of experimental data. According to the evaluation results, artificial neural networks（ ANNs） model to predict the ultimate strength of FRP confined column with...
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| Published in | 东华大学学报：英文版 Vol. 32; no. 3; pp. 395 - 400 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
2015
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| Online Access | Get full text |
| ISSN | 1672-5220 |
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| Summary: | An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer（ FRP） was presented with extensive collection of experimental data. According to the evaluation results, artificial neural networks（ ANNs） model to predict the ultimate strength of FRP confined column with different shapes was proposed. The models had seven inputs including the column length,the tensile strength of the FRP in the hoop direction,the total thickness of FRP,the diameter of the concrete specimen,the elastic modulus of FRP,the corner radius and the concrete compressive strength. The compressive strength of the confined concrete was the output data. The results reveal that the proposed models have good prediction and generalization capacity with acceptable errors. |
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| Bibliography: | An evaluation of existing strength of concrete columns confined with fiber-reinforced polymer（ FRP） was presented with extensive collection of experimental data. According to the evaluation results, artificial neural networks（ ANNs） model to predict the ultimate strength of FRP confined column with different shapes was proposed. The models had seven inputs including the column length,the tensile strength of the FRP in the hoop direction,the total thickness of FRP,the diameter of the concrete specimen,the elastic modulus of FRP,the corner radius and the concrete compressive strength. The compressive strength of the confined concrete was the output data. The results reveal that the proposed models have good prediction and generalization capacity with acceptable errors. CAO Yu-gui , LI Xiao-qing , HU Jun （ College of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China） 31-1920/N compressive strength; concrete column; artificial neuralnetworks （ANN） ; fiber-reinforced polymer （FRP） |
| ISSN: | 1672-5220 |