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Computers and Concrete
  Volume 20, Number 1, July 2017, pages 031-38

Predicting strength of SCC using artificial neural network and multivariable regression analysis
Prasenjit Saha, Prasad M.L.V. and P. Rathish Kumar

    In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN\'s are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.
Key Words
    self-compacting concrete; compressive strength; artificial neural network; multivariable regression analysis; mean absolute error
Prasenjit Saha and Prasad M.L.V.: Department of Civil Engineering, NIT, Silchar-788010, Cachar District, Assam, India
P. Rathish Kumar: Department of Civil Engineering, National Institute of Technology, Warangal, 506004, India

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