Nonlinear modeling of shear strength of SFRC beams using linear genetic programming

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

@Article{Gandomi:2011:SEM,
  author =       "A. H. Gandomi and A. H. Alavi and G. J. Yun",
  title =        "Nonlinear modeling of shear strength of {SFRC} beams
                 using linear genetic programming",
  journal =      "Structural Engineering and Mechanics, An International
                 Journal",
  year =         "2011",
  volume =       "38",
  number =       "1",
  pages =        "1--25",
  month =        apr # " 10",
  keywords =     "genetic algorithms, genetic programming,
                 fiber-reinforced concrete beams, linear genetic
                 programming, SFRC beam, shear strength, formulation.",
  publisher =    "Techno Press, P.O. Box 33, Yuseong, Daejeon 305-600
                 Korea",
  ISSN =         "1225-4568",
  URL =          "http://technopress.kaist.ac.kr/?page=container&journal=sem&volume=38&num=1",
  DOI =          "doi:10.12989/sem.2011.38.1.001",
  abstract =     "A new nonlinear model was developed to evaluate the
                 shear resistance of steel fibre reinforced concrete
                 beams (SFRCB) using linear genetic programming (LGP).
                 The proposed model relates the shear strength to the
                 geometrical and mechanical properties of SFRCB. The
                 best model was selected after developing and
                 controlling several models with different combinations
                 of the influencing parameters. The models were
                 developed using a comprehensive database containing 213
                 test results of SFRC beams without stirrups obtained
                 through an extensive literature review. The database
                 includes experimental results for normal and
                 high-strength concrete beams. To verify the
                 applicability of the proposed model, it was employed to
                 estimate the shear strength of a part of test results
                 that were not included in the modelling process. The
                 external validation of the model was further verified
                 using several statistical criteria recommended by
                 researchers. The contributions of the parameters
                 affecting the shear strength were evaluated through a
                 sensitivity analysis. The results indicate that the LGP
                 model gives precise estimates of the shear strength of
                 SFRCB. The prediction performance of the model is
                 significantly better than several solutions found in
                 the literature. The LGP-based design equation is
                 remarkably straightforward and useful for pre-design
                 applications.",
}

Genetic Programming entries for A H Gandomi A H Alavi Gunjin Yun

Citations