Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming

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@Article{Nazari2011473,
  author =       "Ali Nazari and Shadi Riahi",
  title =        "Prediction split tensile strength and water
                 permeability of high strength concrete containing
                 {TiO2} nanoparticles by artificial neural network and
                 genetic programming",
  journal =      "Composites Part B: Engineering",
  volume =       "42",
  number =       "3",
  pages =        "473--488",
  year =         "2011",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Ceramic-matrix composites
                 (CMCs), Strength, Computational modelling",
  ISSN =         "1359-8368",
  DOI =          "doi:10.1016/j.compositesb.2010.12.004",
  URL =          "http://www.sciencedirect.com/science/article/B6TWK-51P9X22-2/2/c880a08e046f39a96d8b52a6df27266e",
  abstract =     "In the present paper, two models based on artificial
                 neural networks (ANN) and genetic programming (GEP) for
                 predicting split tensile strength and percentage of
                 water absorption of concretes containing TiO2
                 nanoparticles have been developed at different ages of
                 curing. For purpose of 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 are
                 arranged in a format of eight input parameters that
                 cover the cement content (C), nanoparticle content (N),
                 aggregate type (AG), water content (W), the amount of
                 superplasticizer (S), the type of curing medium (CM),
                 Age of curing (AC) and number of testing try (NT).
                 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 TiO2 nanoparticles were
                 predicted. The training and testing results in the
                 neural network and genetic programming models have
                 shown that every two models have strong potential for
                 predicting the split tensile strength and percentage of
                 water absorption values of concretes containing TiO2
                 nanoparticles. It has been found that NN and GEP models
                 will be valid within the ranges of variables. 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

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