Concrete Mix Design Optimization using Artificial Neural Networks

Author(s): Bal Lyes, Buyle-Bodin François
Paper category: Proceedings
Book title: Proceedings of the 2nd International RILEM/COST Conference on Early Age Cracking and Serviceability in Cement-based Materials and Structures Volume 2
Editor(s): Stéphanie Staquet and Dimitrios Aggelis
Publisher: RILEM Publications SARL
Publication year: 2017
Pages: 559-564
Total Pages: 6
Language : English

Abstract: The first objective of this research is to optimize the mixing of concrete and to ensure the strength of concrete by the method of Artificial Neural Network (ANN). These models use a multi layer back propagation. They depend on a very large database (RILEM Data Bank), and an appropriate choice of architectures and learning processes. These models take into account the different parameters of concrete preservation and making which affect properties concrete as: Relative humidity, water to cement ratio, and fine aggregate to total aggregate ratio or sand to total aggregate ratio. The models are correctly adapted for optimizing the appropriate proportions of concrete mix on the basis of two key properties concrete compressive strength and creep. The second objective of this research is to reduce the total number of test of various mixtures.

Online publication : 2017
Publication type : full_text
Public price (Euros) : 0.00

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