Concrete Mix Design Using Artificial Neural Network

Sakshi Gupta

doi:10.15415/jotitt.2013.11003

Authors

Sakshi Gupta National Institute of Technology, Kurukshetra 136118, Haryana, India

DOI:

https://doi.org/10.15415/jotitt.2013.11003

Keywords:

concrete mix, artificial neural network (ANN), 7-days strength, 14-days strength, 28-days compressive strength, fineness modulus, activation function, modeling

Abstract

Concrete mix design is a process based on sound technical principles for proportioning of ingredients in right quantities. This paper demonstrates the applicability of Artificial Neural Networks (ANN) Model for approximate proportioning of concrete mixes. For ANN a trained back propagation neural network is integrated in the model to learn experimental data pertaining to predict 7, 14 and 28-day compressive strength which have been loaded into a model, containing 55 concrete mixtures. The ANN model proposed is based on 5 input parameters such as cement, sand, coarse aggregate, and water and fineness modulus. The proposed concrete mix proportion design is expected to reduce the number of trials in laboratory as well as field, saves cost of material as well as labor and also saves time as it provides higher accuracy. The concrete designed is expected to have higher durability and hence is economical.

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