Genetic programming for moment capacity modeling of ferrocement members

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@Article{Gandomi:2013:EngStruct,
  author =       "Amir H. Gandomi and David A. Roke and Kallol Sett",
  title =        "Genetic programming for moment capacity modeling of
                 ferrocement members",
  journal =      "Engineering Structures",
  year =         "2013",
  volume =       "57",
  pages =        "169--176",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Moment capacity, Ferrocement
                 members",
  ISSN =         "0141-0296",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0141029613004343",
  DOI =          "doi:10.1016/j.engstruct.2013.09.022",
  size =         "8 pages",
  abstract =     "In this study, a robust variant of genetic programming
                 called gene expression programming (GEP) is used to
                 predict the moment capacity of ferrocement members.
                 Constitutive relationships were obtained to correlate
                 the ultimate moment capacity with mechanical and
                 geometrical parameters using previously published
                 experimental results. A subsequent parametric analysis
                 was carried out and the trends of the results were
                 confirmed. A comparative study was conducted between
                 the results obtained by the proposed models and those
                 of the plastic analysis, mechanism and nonlinear
                 regression approaches, as well as two black-box models:
                 back-propagation neural networks (BPNN) and an adaptive
                 neuro-fuzzy inference system (ANFIS). Three GEP models
                 are developed to capture the effect of randomising the
                 test data subsets used to develop the models. The
                 results indicate that the GEP models accurately
                 estimate the moment capacity of ferrocement members.
                 The prediction performance of the GEP models is
                 significantly better than the plastic analysis,
                 mechanism and nonlinear regression approaches and is
                 comparable to that of the BPNN and ANFIS models.",
}

Genetic Programming entries for A H Gandomi David Roke Kallol Sett

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