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Advances in Materials Science and Engineering
Volume 2013, Article ID 527089, 10 pages
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.


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.