Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete
This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/527089 |
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| _version_ | 1832545530927906816 |
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| author | Sangyong Kim Hee-Bok Choi Yoonseok Shin Gwang-Hee Kim Deok-Seok Seo |
| author_facet | Sangyong Kim Hee-Bok Choi Yoonseok Shin Gwang-Hee Kim Deok-Seok Seo |
| author_sort | Sangyong Kim |
| collection | DOAJ |
| description | This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that the proposed method, which is NN based on GA, is proper for optimizing appropriate mixing proportion of RAC. Also, this method would help the construction engineers to utilize the recycled aggregate and reduce the concrete waste in construction process. |
| format | Article |
| id | doaj-art-a2cebe3bb38b44868336a1413c2a9ad1 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-a2cebe3bb38b44868336a1413c2a9ad12025-02-03T07:25:33ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422013-01-01201310.1155/2013/527089527089Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate ConcreteSangyong Kim0Hee-Bok Choi1Yoonseok Shin2Gwang-Hee Kim3Deok-Seok Seo4School of Construction Management and Engineering, University of Reading, Reading RG6 6AW, UKDepartment of Architectural Engineering, Jeju National University, Jeju 690-756, Republic of KoreaDepartment of Plant & Architectural Engineering, Kyonggi University, Gwanggyosan-Ro, Yeongtong-Gu, Suwon-Si, Gyeonggi-Do 443-760, Republic of KoreaDepartment of Plant & Architectural Engineering, Kyonggi University, Gwanggyosan-Ro, Yeongtong-Gu, Suwon-Si, Gyeonggi-Do 443-760, Republic of KoreaDepartment of Architectural Engineering, Halla University, Wonju-Si 220-712, Republic of KoreaThis research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that the proposed method, which is NN based on GA, is proper for optimizing appropriate mixing proportion of RAC. Also, this method would help the construction engineers to utilize the recycled aggregate and reduce the concrete waste in construction process.http://dx.doi.org/10.1155/2013/527089 |
| spellingShingle | Sangyong Kim Hee-Bok Choi Yoonseok Shin Gwang-Hee Kim Deok-Seok Seo Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete Advances in Materials Science and Engineering |
| title | Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete |
| title_full | Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete |
| title_fullStr | Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete |
| title_full_unstemmed | Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete |
| title_short | Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete |
| title_sort | optimizing the mixing proportion with neural networks based on genetic algorithms for recycled aggregate concrete |
| url | http://dx.doi.org/10.1155/2013/527089 |
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