Artificial Neural Network for Concrete Mix Design
Keywords:
Artificial neural networks (ANNs), compressive, FFBP, CFBP, predictingAbstract
Artificial neural networks (ANNs) are being increasingly used for predicting various civil engineering characteristics, such as the prediction of compressive strength of concrete of various grades, fracture toughness, and determining the displacement in concrete reinforcement buildings, etc. In this study, comparison is made between feed forward back propagation (FFBP) and Cascade forward back propagation (CFBP) algorithms for concrete mix design. ANN models have been developed using cement quantity, fine aggregate, metal, water, super plasticizer, and aggregate cement ratio by weight as the input variables to forecast the compressive strength of concrete for 3, 7, and 28 days. The training and testing datasets were split into 50% and 70% respectively. The results revealed that both FFBP and CFBP algorithms are successful models for predicting the compressive strength, but the training dataset of 70% and FFBP algorithm gave more accurate results.
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