Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete
Joint Authors
Seo, Deok-Seok
Choi, Hee-Bok
Kim, Sangyong
Shin, Yoonseok
Kim, Gwang-Hee
Source
Advances in Materials Science and Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-13
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Engineering Sciences and Information Technology
Abstract EN
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.
American Psychological Association (APA)
Kim, Sangyong& Choi, Hee-Bok& Shin, Yoonseok& Kim, Gwang-Hee& Seo, Deok-Seok. 2013. Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete. Advances in Materials Science and Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-478757
Modern Language Association (MLA)
Kim, Sangyong…[et al.]. Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete. Advances in Materials Science and Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-478757
American Medical Association (AMA)
Kim, Sangyong& Choi, Hee-Bok& Shin, Yoonseok& Kim, Gwang-Hee& Seo, Deok-Seok. Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete. Advances in Materials Science and Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-478757
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-478757