Open AccessOptimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete
Author(s) -
Sangyong Kim,
Heebok Choi,
Yoonseok Shin,
Gwang-Hee Kim,
DeokSeok Seo
Publication year - 2013
Publication title -
advances in materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 42
eISSN - 1687-8442
pISSN - 1687-8434
DOI - 10.1155/2013/527089
Subject(s) - mixing (physics) , aggregate (composite) , materials science , compressive strength , artificial neural network , sensitivity (control systems) , genetic algorithm , mixing ratio , process (computing) , composite material , process engineering , computer science , machine learning , thermodynamics , engineering , physics , quantum mechanics , electronic engineering , operating system
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
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