Research Articles | Challenge Journal of Concrete Research Letters

Modelling Compressive Strength of Recycled Aggregate Concrete Using Neural Networks and Regression

Neela Deshpande, Shreenivas Londhe
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Abstract


Recycled aggregates are used in concrete and is called as Recycled Aggregate Concrete (RAC). This paper aims at predicting the 28 day compressive strength of RAC using two techniques namely Artificial Neural Network (ANN) and Non-linear regression (NLR). Five ANN and NLR models were developed with input parameters as  per cubic proportions of cement, sand, natural coarse aggregate, recycled coarse aggregate, water , admixtures  used in the mix designs and non-dimensional parameters sand aggregate ratio, water to total materials ratio and the replacement ratio of recycled coarse aggregates to natural aggregates(by volume) in concrete. The effects of each parameter on networks in both techniques were studied. Comparing the techniques shows that ANN performs better than NLR equations. With limited amount of input parameters also, ANN1 predicted the strength of RAC better as compared to NLR1. The performance of ANN models and NLR models improved with the use of non-dimensional parameters.

Keywords


recycled concrete aggregates; artificial neural networks; non-linear regression; 28 days compressive strength; input parameters and non-dimensional parameters

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