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Advances in Materials Science and Engineering
Volume 2013, Article ID 527089, 10 pages
http://dx.doi.org/10.1155/2013/527089
Research Article

Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete

1School of Construction Management and Engineering, University of Reading, Reading RG6 6AW, UK
2Department of Architectural Engineering, Jeju National University, Jeju 690-756, Republic of Korea
3Department of Plant & Architectural Engineering, Kyonggi University, Gwanggyosan-Ro, Yeongtong-Gu, Suwon-Si, Gyeonggi-Do 443-760, Republic of Korea
4Department of Architectural Engineering, Halla University, Wonju-Si 220-712, Republic of Korea

Received 1 May 2013; Accepted 8 July 2013

Academic Editor: Alex Li

Copyright © 2013 Sangyong Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Sangyong Kim, Hee-Bok Choi, Yoonseok Shin, Gwang-Hee Kim, and Deok-Seok Seo, “Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete,” Advances in Materials Science and Engineering, vol. 2013, Article ID 527089, 10 pages, 2013. https://doi.org/10.1155/2013/527089.