A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

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@Article{journals/nca/AminianJAGE11,
  author =       "Pejman Aminian and Mohamad Reza Javid and 
                 Abazar Asghari and Amir Hossein Gandomi and 
                 Milad {Arab Esmaeili}",
  title =        "A robust predictive model for base shear of steel
                 frame structures using a hybrid genetic programming and
                 simulated annealing method",
  journal =      "Neural Computing and Applications",
  year =         "2011",
  number =       "8",
  volume =       "20",
  pages =        "1321--1332",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, base shear,
                 steel frame structures, simulated annealing, nonlinear
                 modelling",
  ISSN =         "0941-0643",
  DOI =          "doi:10.1007/s00521-011-0689-0",
  size =         "12 pages",
  abstract =     "This study presents a new empirical model to estimate
                 the base shear of plane steel structures subjected to
                 earthquake load using a hybrid method integrating
                 genetic programming (GP) and simulated annealing (SA),
                 called GP/SA. The base shear of steel frames was
                 formulated in terms of the number of bays, number of
                 storey, soil type, and situation of braced or unbraced.
                 A classical GP model was developed to benchmark the
                 GP/SA model. The comprehensive database used for the
                 development of the correlations was obtained from
                 finite element analysis. A parametric analysis was
                 carried out to evaluate the sensitivity of the base
                 shear to the variation of the influencing parameters.
                 The GP/SA and classical GP correlations provide a
                 better prediction performance than the widely used UBC
                 code and a neural network-based model found in the
                 literature. The developed correlations may be used as
                 quick checks on solutions developed by deterministic
                 analyses.",
  notes =        "Special Issue: ISNN 2010",
  affiliation =  "Department of Civil Engineering, Islamic Azad
                 University, Shahrood Branch, Shahrood, Iran",
  bibdate =      "2011-10-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca20.html#AminianJAGE11",
}

Genetic Programming entries for Pejman Aminian Mohamad Reza Javid Abazar Asghari A H Gandomi Milad Arab Esmaeili

Citations