Title:
Application of Neural Networks for Proportioning of Concrete Mixes
Author(s):
Ju-Wo Oh, In-Won Lee, Ju-Tae Kim, and Gyu-Won Lee
Publication:
Materials Journal
Volume:
96
Issue:
1
Appears on pages(s):
61-67
Keywords:
concrete; mix proportioning; neural network
DOI:
10.14359/429
Date:
1/1/1999
Abstract:
In determining proportioning of concrete mixes, we need the information of the codes, the specifications, and the experiences of experts. However, we cannot consider all factors regarding mix proportioning. Therefore, the final acceptance depends on concrete quality control test results. In this process, we meet the uncertainties of materials, temperature, site environmental situations, personal skilfulness, and errors in calculations and testing processes. Adjustments must then be made for a proper proportioning. This kind of concrete mix proportioning and adjustments are somewhat complicated, time-consuming, and uncertain tasks. In this paper, as a tool to minimize the uncertainties and errors of proportioning concrete mixes, an artificial neural network is used. The required compressive strengths and also the actual compressive strengths with variations obtainable from the final compressive strength test are used to train and test the network. The results show that neural networks have a strong potential as a tool for concrete mix proportioning.