Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete
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 sensitivi...
Saved in:
Main Authors: | Sangyong Kim, Hee-Bok Choi, Yoonseok Shin, Gwang-Hee Kim, Deok-Seok Seo |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2013-01-01
|
Series: | Advances in Materials Science and Engineering |
Online Access: | http://dx.doi.org/10.1155/2013/527089 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Life Comparative Analysis of Energy Consumption and CO2 Emissions of Different Building Structural Frame Types
by: Sangyong Kim, et al.
Published: (2013-01-01) -
Effects of Recycled Aggregate on Concrete Mix and Exposure to Chloride
by: Ammar Ben Nakhi, et al.
Published: (2019-01-01) -
Privacy Protection for Personal Health Device Communication and Healthcare Building Applications
by: Soon Seok Kim, et al.
Published: (2014-01-01) -
Experimental Testing and Numerical Simulation of Recycled Concrete Aggregate in a Concrete Mix
by: Bini Neupane, et al.
Published: (2025-01-01) -
Durability of Concrete with Recycled Aggregate
by: Helsing Elisabeth, et al.
Published: (2024-12-01)