An empirical model for shear capacity of RC deep beams using genetic-simulated annealing

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "A. H. Gandomi and A. H. Alavi and 
                 D. Mohammadzadeh Shadmehri and M. G. Sahab",
  title =        "An empirical model for shear capacity of {RC} deep
                 beams using genetic-simulated annealing",
  journal =      "Archives of Civil and Mechanical Engineering",
  year =         "2013",
  volume =       "13",
  number =       "3",
  pages =        "354--369",
  keywords =     "genetic algorithms, genetic programming, Shear
                 capacity, RC deep beam, Genetic-simulated annealing,
                 Empirical formula",
  ISSN =         "1644-9665",
  DOI =          "doi:10.1016/j.acme.2013.02.007",
  URL =          "",
  size =         "16 pages",
  abstract =     "This paper presents an empirical model to predict the
                 shear strength of RC deep beams. A hybrid search
                 algorithm coupling genetic programming (GP) and
                 simulated annealing (SA), called genetic simulated
                 annealing (GSA), was used to develop mathematical
                 relationship between the experimental data. Using this
                 algorithm, a constitutive relationship was obtained to
                 make pertinent the shear strength of deep beams to nine
                 mechanical and geometrical parameters. The model was
                 developed using an experimental database acquired from
                 the literature. The results indicate that the proposed
                 empirical model is properly capable of evaluating the
                 shear strength of deep beams. The validity of the
                 proposed model was examined by comparing its results
                 with those obtained from American Concrete Institute
                 (ACI) and Canadian Standard Association (CSA) codes.
                 The derived equation is notably simple and includes
                 several effective parameters.",

Genetic Programming entries for A H Gandomi A H Alavi D Mohammadzadeh Shadmehri Mohammad Ghasem Sahab