Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres
The study presents a comparative approach between Response Surface Methodology (RSM) and hybridized Genetic Algorithm of Artificial Neural Network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength, split tensile strength and slump for steel fibre reinforced concret...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2019.1649852 |
| _version_ | 1797718752678117376 |
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| author | Temitope F. Awolusi Oluwaseyi L. Oke Olufunke O. Akinkurolere Olumoyewa D. Atoyebi |
| author_facet | Temitope F. Awolusi Oluwaseyi L. Oke Olufunke O. Akinkurolere Olumoyewa D. Atoyebi |
| author_sort | Temitope F. Awolusi |
| collection | DOAJ |
| description | The study presents a comparative approach between Response Surface Methodology (RSM) and hybridized Genetic Algorithm of Artificial Neural Network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength, split tensile strength and slump for steel fibre reinforced concrete. The effects of process variables such as aspect ratio, water–cement ratio and cement content were investigated using the central composite design of response surface methodology. This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies was compared using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Model Predictive Error (MPE) and Absolute Average Deviation (AAD). The response surface methodology model was found more accurate in being able to predict compared to the hybridized genetic algorithm of the artificial neural network. |
| first_indexed | 2024-03-12T08:54:58Z |
| format | Article |
| id | doaj.art-81a52b9c4d5d4ba2adfbb0c18330554c |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T08:54:58Z |
| publishDate | 2019-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-81a52b9c4d5d4ba2adfbb0c18330554c2023-09-02T16:06:07ZengTaylor & Francis GroupCogent Engineering2331-19162019-01-016110.1080/23311916.2019.16498521649852Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyresTemitope F. Awolusi0Oluwaseyi L. Oke1Olufunke O. Akinkurolere2Olumoyewa D. Atoyebi3Ekiti State UniversityEkiti State UniversityEkiti State UniversityLandmark UniversityThe study presents a comparative approach between Response Surface Methodology (RSM) and hybridized Genetic Algorithm of Artificial Neural Network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength, split tensile strength and slump for steel fibre reinforced concrete. The effects of process variables such as aspect ratio, water–cement ratio and cement content were investigated using the central composite design of response surface methodology. This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies was compared using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Model Predictive Error (MPE) and Absolute Average Deviation (AAD). The response surface methodology model was found more accurate in being able to predict compared to the hybridized genetic algorithm of the artificial neural network.http://dx.doi.org/10.1080/23311916.2019.1649852response surface methodologyhybridgenetic algorithm artificial neural networkconcreteflexural strengthsteel fibre reinforced concretecivil engineering |
| spellingShingle | Temitope F. Awolusi Oluwaseyi L. Oke Olufunke O. Akinkurolere Olumoyewa D. Atoyebi Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres Cogent Engineering response surface methodology hybrid genetic algorithm artificial neural network concrete flexural strength steel fibre reinforced concrete civil engineering |
| title | Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres |
| title_full | Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres |
| title_fullStr | Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres |
| title_full_unstemmed | Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres |
| title_short | Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres |
| title_sort | comparison of response surface methodology and hybrid training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres |
| topic | response surface methodology hybrid genetic algorithm artificial neural network concrete flexural strength steel fibre reinforced concrete civil engineering |
| url | http://dx.doi.org/10.1080/23311916.2019.1649852 |
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