Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning
This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2017/9897078 |
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| _version_ | 1850209275144568832 |
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| author | Nhat-Duc Hoang Duy-Thang Vu Xuan-Linh Tran Van-Duc Tran |
| author_facet | Nhat-Duc Hoang Duy-Thang Vu Xuan-Linh Tran Van-Duc Tran |
| author_sort | Nhat-Duc Hoang |
| collection | DOAJ |
| description | This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs. |
| format | Article |
| id | doaj-art-922e5657a8794ed2b3a7dcf58fed53fc |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-922e5657a8794ed2b3a7dcf58fed53fc2025-08-20T02:10:03ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322017-01-01201710.1155/2017/98970789897078Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network LearningNhat-Duc Hoang0Duy-Thang Vu1Xuan-Linh Tran2Van-Duc Tran3Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, K7/25 Quang Trung, Danang, VietnamFaculty of Architecture, Duy Tan University, K7/25 Quang Trung, Danang, VietnamInstitute of Research and Development, Faculty of Civil Engineering, Duy Tan University, K7/25 Quang Trung, Danang, VietnamInternational School, Duy Tan University, 254 Nguyen Van Linh, Danang 550000, VietnamThis study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs.http://dx.doi.org/10.1155/2017/9897078 |
| spellingShingle | Nhat-Duc Hoang Duy-Thang Vu Xuan-Linh Tran Van-Duc Tran Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning Applied Computational Intelligence and Soft Computing |
| title | Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning |
| title_full | Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning |
| title_fullStr | Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning |
| title_full_unstemmed | Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning |
| title_short | Modeling Punching Shear Capacity of Fiber-Reinforced Polymer Concrete Slabs: A Comparative Study of Instance-Based and Neural Network Learning |
| title_sort | modeling punching shear capacity of fiber reinforced polymer concrete slabs a comparative study of instance based and neural network learning |
| url | http://dx.doi.org/10.1155/2017/9897078 |
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