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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2013

01-01-2013 | Original Article

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

Authors: Hasbi Yaprak, Abdülkadir Karacı, İlhami Demir

Published in: Neural Computing and Applications | Issue 1/2013

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Abstract

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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Metadata
Title
Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks
Authors
Hasbi Yaprak
Abdülkadir Karacı
İlhami Demir
Publication date
01-01-2013
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0671-x

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