Computer-aided Prediction of the ZrO2 Nanoparticles' Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Nazari201283,
  author =       "Ali Nazari and Shadi Riahi",
  title =        "Computer-aided Prediction of the ZrO2 Nanoparticles'
                 Effects on Tensile Strength and Percentage of Water
                 Absorption of Concrete Specimens",
  journal =      "Journal of Materials Science \& Technology",
  volume =       "28",
  number =       "1",
  pages =        "83--96",
  year =         "2012",
  ISSN =         "1005-0302",
  DOI =          "doi:10.1016/S1005-0302(12)60027-9",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1005030212600279",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Concrete, Curing medium, ZrO2
                 nanoparticles, Artificial neural network, Split tensile
                 strength, Percentage of water absorption",
  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