Genetic-based modeling of uplift capacity of suction caissons

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Alavi2011,
  author =       "Amir Hossein Alavi and Pejman Aminian and 
                 Amir Hossein Gandomi and Milad {Arab Esmaeili}",
  title =        "Genetic-based modeling of uplift capacity of suction
                 caissons",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "10",
  pages =        "12608--12618",
  year =         "2011",
  month =        "15 " # sep,
  ISSN =         "0957-4174",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417411005653",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-52P1KNK-4/2/f33267200d0fc51ad7a086befe3a361c",
  DOI =          "doi:10.1016/j.eswa.2011.04.049",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, Suction caissons, Uplift
                 capacity, Formulation",
  size =         "11 pages",
  abstract =     "In this study, classical tree-based genetic
                 programming (TGP) and its recent variants, namely
                 linear genetic programming (LGP) and gene expression
                 programming (GEP) are used to develop new prediction
                 equations for the uplift capacity of suction caissons.
                 The uplift capacity is formulated in terms of several
                 inflecting variables. An experimental database obtained
                 from the literature is employed to develop the models.
                 Further, a conventional statistical analysis is
                 performed to benchmark the proposed models. Sensitivity
                 and parametric analyses are conducted to verify the
                 results. TGP, LGP and GEP are found to be effective
                 methods for evaluating the horizontal, vertical, and
                 inclined uplift capacity of suction caissons. The TGP,
                 LGP and GEP models reach a prediction performance
                 better than or comparable with the models found in the
                 literature.",
}

Genetic Programming entries for A H Alavi Pejman Aminian A H Gandomi Milad Arab Esmaeili

Citations