Computer-aided Prediction of the ZrO2 Nanoparticles' Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens
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- @Article{Nazari201283,
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author = "Ali Nazari and Shadi Riahi",
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title = "Computer-aided Prediction of the ZrO2 Nanoparticles'
Effects on Tensile Strength and Percentage of Water
Absorption of Concrete Specimens",
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journal = "Journal of Materials Science \& Technology",
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volume = "28",
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number = "1",
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pages = "83--96",
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year = "2012",
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ISSN = "1005-0302",
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DOI = "doi:10.1016/S1005-0302(12)60027-9",
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URL = "http://www.sciencedirect.com/science/article/pii/S1005030212600279",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, Concrete, Curing medium, ZrO2
nanoparticles, Artificial neural network, Split tensile
strength, Percentage of water absorption",
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abstract = "In the present paper, two models based on artificial
neural networks and genetic programming for predicting
split tensile strength and percentage of water
absorption of concretes containing ZrO2 nanoparticles
have been developed at different ages of curing. For
building these models, training and testing using
experimental results for 144 specimens produced with 16
different mixture proportions were conducted. The data
used in the multilayer feed forward neural networks
models and input variables of genetic programming
models were arranged in a format of eight input
parameters that cover the cement content, nanoparticle
content, aggregate type, water content, the amount of
superplasticiser, the type of curing medium, age of
curing and number of testing try. According to these
input parameters, in the neural networks and genetic
programming models, the split tensile strength and
percentage of water absorption values of concretes
containing ZrO2 nanoparticles were predicted. The
training and testing results in the neural network and
genetic programming models have shown that two models
have strong potential for predicting the split tensile
strength and percentage of water absorption values of
concretes containing ZrO2 nanoparticles. It has been
found that neural network (NN) and gene expression
programming (GEP) models will be valid within the
ranges of variables. In neural networks model, as the
training and testing ended when minimum error norm of
network gained, the best results were obtained and in
genetic programming model, when 4 genes were selected
to construct the model, the best results were acquired.
Although neural network have predicted better results,
genetic programming is able to predict reasonable
values with a simpler method rather than neural
network.",
- }
Genetic Programming entries for
Ali Nazari
Shadi Riahi
Citations