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