Empirical modelling of shear strength of RC deep beams by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Ashour:2003:CS,
  author =       "A. F. Ashour and L. F. Alvarez and V. V. Toropov",
  title =        "Empirical modelling of shear strength of RC deep beams
                 by genetic programming",
  journal =      "Computers and Structures",
  year =         "2003",
  volume =       "81",
  number =       "5",
  pages =        "331--338",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Reinforced
                 concrete deep beams, Empirical model building",
  URL =          "http://www.sciencedirect.com/science/article/B6V28-47S6J5M-5/2/03211d57903fd1d7c48ac56fb32d1d36",
  DOI =          "doi:10.1016/S0045-7949(02)00437-6",
  abstract =     "This paper investigates the feasibility of using
                 previous termgeneticnext term programming (GP) to
                 create an empirical model for the complicated
                 non-linear relationship between various input
                 parameters associated with reinforced concrete (RC)
                 deep beams and their ultimate shear strength. GP is a
                 relatively new form of artificial intelligence, and is
                 based on the ideas of Darwinian theory of evolution and
                 previous termgenetics.next term The size and structural
                 complexity of the empirical model are not specified in
                 advance, but these characteristics evolve as part of
                 the prediction. The engineering knowledge on RC deep
                 beams is also included in the search process through
                 the use of appropriate mathematical functions. The
                 model produced by GP is constructed directly from a set
                 of experimental results available in the literature.
                 The validity of the obtained model is examined by
                 comparing its response with the shear strength of the
                 training and other additional datasets. The developed
                 model is then used to study the relationships between
                 the shear strength and different influencing
                 parameters. The predictions obtained from GP agree well
                 with experimental observations.",
}

Genetic Programming entries for A F Ashour Luis F Alvarez Vassili V Toropov

Citations