New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach

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@Article{Aminian:2013:NCA,
  author =       "Pejman Aminian and Hadi Niroomand and 
                 Amir Hossein Gandomi and Amir Hossein Alavi and 
                 Milad {Arab Esmaeili}",
  title =        "New design equations for assessment of load carrying
                 capacity of castellated steel beams: a machine learning
                 approach",
  journal =      "Neural Computing and Applications",
  year =         "2013",
  volume =       "23",
  number =       "1",
  pages =        "119--131",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Linear
                 genetic programming, Castellated beam, Load carrying
                 capacity, Simulated annealing, Formulation",
  publisher =    "Springer",
  language =     "English",
  ISSN =         "0941-0643",
  URL =          "http://link.springer.com/article/10.1007%2Fs00521-012-1138-4",
  DOI =          "doi:10.1007/s00521-012-1138-4",
  size =         "13 pages",
  abstract =     "This paper presents an innovative machine learning
                 approach for the formulation of load carrying capacity
                 of castellated steel beams (CSB). New design equations
                 were developed to predict the load carrying capacity of
                 CSB using linear genetic programming (LGP), and an
                 integrated search algorithm of genetic programming and
                 simulated annealing, called GSA. The load capacity was
                 formulated in terms of the geometrical and mechanical
                 properties of the castellated beams. An extensive trial
                 study was carried out to select the most relevant input
                 variables for the LGP and GSA models. A comprehensive
                 database was gathered from the literature to develop
                 the models. The generalisation capabilities of the
                 models were verified via several criteria. The
                 sensitivity of the failure load of CSB to the
                 influencing variables was examined and discussed. The
                 employed machine learning systems were found to be
                 effective methods for evaluating the failure load of
                 CSB. The prediction performance of the optimal LGP
                 model was found to be better than that of the GSA
                 model.",
}

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

Citations