Computer-aided design of the effects of Fe2O3 nanoparticles on split tensile strength and water permeability of high strength concrete

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@Article{Nazari20113966,
  author =       "Ali Nazari and Shadi Riahi",
  title =        "Computer-aided design of the effects of {Fe2O3}
                 nanoparticles on split tensile strength and water
                 permeability of high strength concrete",
  journal =      "Material \& Design",
  volume =       "32",
  number =       "7",
  pages =        "3966--3979",
  year =         "2011",
  ISSN =         "0261-3069",
  DOI =          "doi:10.1016/j.matdes.2011.01.064",
  URL =          "http://www.sciencedirect.com/science/article/B6TX5-52F88YN-5/2/1cb3e97f2108ac3b0aeec50be6ccb86f",
  keywords =     "genetic algorithms, genetic programming, A. Ceramic
                 matrix composites, E. Mechanical, E. Physical",
  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 Fe2O3 nanoparticles
                 have been developed. To build these models, training
                 and testing of the network by using experimental
                 results from 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 have been
                 arranged in a format of eight input parameters that
                 cover the cement content, nanoparticle content,
                 aggregate type, water content, the amount of
                 superplasticizer, the type of curing medium, age of
                 curing and number of testing try. According to these
                 input parameters, in the two models, the split tensile
                 strength and percentage of water absorption values of
                 concretes containing Fe2O3 nanoparticles were
                 predicted. The training and testing results in the
                 neural network and genetic programming models have
                 shown that every two models are of strong potential for
                 predicting the split tensile strength and percentage of
                 water absorption values of concretes containing Fe2O3
                 nanoparticles. Although neural network has 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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