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Evaluation of the Flow Properties of Self-Compacting Fly-ash Concrete Using Artificial Neural Network

Temple Nwofor, L.A. Joshua

Abstract


The purpose of this study is to examine the feasibility of using artificial neural network programming for the calculation of the fresh properties of self-compacting concrete. The input parameters of the neural network were the mix composition influencing the fresh properties of self-compacting concrete namely; the cement content, the dosages of fly-ash, water, fine aggregate, coarse aggregate, and superplasticizer. The model is founded on a multilayer feed forward neural network. The details of the proposed ANN with its architecture, training and validation were presented in this paper. The output data of the ANN model related to the fresh properties is the blocking ratio (L-box) of forty (40) concrete mixes, six concrete mixes with 0%, 10%, 20%, 30%, 40% and 50% cement replacement with fly ash experimented on and thirty-four mixes from previous works. The effectiveness of the trained ANN was evaluated by comparing its responses (predicted results) with the experimental data that were used in the training process. ANN performed well and provided regression (R) of 0.8636 for L-box blocking ratio; the root mean square error between the output from the experimental data and the Artificial Neural Network predicted data was 0.064 which shows that all the experimental data and predicted data have close relationships. The results show that the ANN model can predict the fresh properties of SCC. 

 

Keywords: Artificial Neural Network, Concrete, L-box, Mix proportion.


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References


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