An evolutionary approach for modeling of shear strength of RC deep beams

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  author =       "Amir Hossein Gandomi and Gun Jin Yun and 
                 Amir Hossein Alavi",
  title =        "An evolutionary approach for modeling of shear
                 strength of RC deep beams",
  journal =      "Materials and Structures",
  year =         "2013",
  volume =       "46",
  number =       "12",
  pages =        "2109--2119",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, Shear strength, RC deep beams",
  publisher =    "Springer Netherlands",
  ISSN =         "1359-5997",
  DOI =          "doi:10.1617/s11527-013-0039-z",
  language =     "English",
  size =         "11 pages",
  abstract =     "In this study, a new variant of genetic programming,
                 namely gene expression programming (GEP) is used to
                 predict the shear strength of reinforced concrete (RC)
                 deep beams. A constitutive relationship was obtained
                 correlating the ultimate load with seven mechanical and
                 geometrical parameters. The model was developed using
                 214 experimental test results obtained from previously
                 published papers. A comparative study was conducted
                 between the results obtained by the proposed model and
                 those of the American Concrete Institute (ACI) and
                 Canadian Standard Association (CSA) models, as well as
                 an Artificial Neural Network (ANN)-based model. A
                 subsequent parametric analysis was carried out and the
                 trends of the results were confirmed via some previous
                 laboratory studies. The results indicate that the GEP
                 model gives precise estimations of the shear strength
                 of RC deep beams. The prediction performance of the
                 model is significantly better than the ACI and CSA
                 models and has a very good agreement with the ANN
                 results. The derived design equation provides a
                 valuable analysis tool accessible to practising

Genetic Programming entries for A H Gandomi Gunjin Yun A H Alavi