Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence
This paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sour...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/3012139 |
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| _version_ | 1832567851265818624 |
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| author | Tu T. Nguyen Hoa Pham Duy Tung Pham Thanh Hoang Hiep Vu |
| author_facet | Tu T. Nguyen Hoa Pham Duy Tung Pham Thanh Hoang Hiep Vu |
| author_sort | Tu T. Nguyen |
| collection | DOAJ |
| description | This paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R2) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC. |
| format | Article |
| id | doaj-art-2db3857d26904be9b46f7819c519ef54 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-2db3857d26904be9b46f7819c519ef542025-02-03T01:00:20ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/30121393012139Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial IntelligenceTu T. Nguyen0Hoa Pham Duy1Tung Pham Thanh2Hoang Hiep Vu3Faculty of Civil Engineering, Hanoi Architectural University, Hanoi, VietnamFaculty of Bridge and Roads, National University of Civil Engineering, Hanoi, VietnamFaculty of Building and Industrial Construction, National University of Civil Engineering, Hanoi, VietnamFaculty of Civil Engineering, Hanoi Architectural University, Hanoi, VietnamThis paper describes the application of two artificial intelligence- (AI-) based methods to predict the 28-day compressive strength of fiber-reinforced high-strength self-compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R2) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC.http://dx.doi.org/10.1155/2020/3012139 |
| spellingShingle | Tu T. Nguyen Hoa Pham Duy Tung Pham Thanh Hoang Hiep Vu Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence Advances in Civil Engineering |
| title | Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence |
| title_full | Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence |
| title_fullStr | Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence |
| title_full_unstemmed | Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence |
| title_short | Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence |
| title_sort | compressive strength evaluation of fiber reinforced high strength self compacting concrete with artificial intelligence |
| url | http://dx.doi.org/10.1155/2020/3012139 |
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