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

Title
Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete
Author(s)
김상용최희복[최희복]신윤석[신윤석]김광희[김광희]서덕석[서덕석]
Keywords
COST ESTIMATION; CONSTRUCTION; RESISTANCE; MODEL
Issue Date
201312
Publisher
HINDAWI PUBLISHING CORPORATION
Citation
ADVANCES IN MATERIALS SCIENCE AND ENGINEERING
Abstract
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.
URI
http://hdl.handle.net/YU.REPOSITORY/28132http://dx.doi.org/10.1155/2013/527089
ISSN
1687-8434
Appears in Collections:
건축학부 > 건축학부 > Articles
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