Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks


The present study aims at developing an artificial neural network (ANN) to predict the compressive strength of concrete. A data set containing a total of 72 concrete samples was used in the study. The following constituted the concrete mixture parameters: two distinct w/c ratios (0.63 and 0.70), three different types of cements and three different cure conditions. Measurement of compressive strengths was performed at 3, 7, 28 and 90 days. Two different ANN models were developed, one with 4 input and 1 output layers, 9 neurons and 1 hidden layer, and the other with 5, 6 neurons, 2 hidden layers. For the training of the developed models, 60 experimental data sets obtained prior to the process were used. The 12 experimental data not used in the training stage were utilized to test ANN models. The researchers have reached the conclusion that ANN provides a good alternative to the existing compressive strength prediction methods, where different cements, ages and cure conditions were used as input parameters.

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Correspondence to Hasbi Yaprak.

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Yaprak, H., Karacı, A. & Demir, İ. Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks. Neural Comput & Applic 22, 133–141 (2013).

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  • Artificial neural network
  • Cement
  • Compressive strength
  • Cure conditions
  • Age