Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete

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

@Article{Nazari:2012:MR,
  title =        "Prediction the effects of {ZnO2} nanoparticles on
                 splitting tensile strength and water absorption of high
                 strength concrete",
  author =       "Ali Nazari and Tohid Azimzadegan",
  journal =      "Materials Research",
  publisher =    "ABM, ABC, ABPol",
  year =         "2012",
  keywords =     "genetic algorithms, genetic programming, neural
                 networks, gene expression programming, nanoparticles,
                 concrete, tensile test, water permeability",
  ISSN =         "15161439",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:67aaaae2ca87020d0fa93f9056e0df78",
  URL =          "http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392012000300016&lng=en&nrm=iso&tlng=en",
  DOI =          "DOI:10.1590/S1516-14392012005000057",
  size =         "15 pages",
  abstract =     "In the present paper, two models based on artificial
                 neural networks (ANN) and gene expression programming
                 (GEP) for predicting splitting tensile strength and
                 water absorption of concretes containing ZnO2
                 nanoparticles at different ages of curing have been
                 developed. To build these models, training and testing
                 using experimental results for 144 specimens produced
                 with 16 different mixture proportions were conducted.
                 The used data 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 splitting tensile strength and
                 water absorption values of concretes containing ZnO2
                 nanoparticles were predicted. The training and testing
                 results in these two models have shown the strong
                 potential of the models for predicting the splitting
                 tensile strength and water absorption values of
                 concretes containing ZnO2 nanoparticles. Although
                 neural networks have predicted better results, genetic
                 programming is able to predict reasonable values with a
                 simpler method rather than neural networks.",
  notes =        "July 2014 doi on scielo.br web page appears to be
                 wrong",
}

Genetic Programming entries for Ali Nazari Tohid Azimzadegan

Citations