Development of prediction models for shear strength of SFRCB using a machine learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Masoud Sarveghadi and Amir H. Gandomi and 
                 Hamed Bolandi and Amir H. Alavi",
  title =        "Development of prediction models for shear strength of
                 {SFRCB} using a machine learning approach",
  journal =      "Neural Computing and Applications",
  keywords =     "genetic algorithms, genetic programming, SFRCB,
                 Multi-expression programming, Shear strength,
  ISSN =         "0941-0643",
  DOI =          "doi:10.1007/s00521-015-1997-6",
  abstract =     "In this study, new design equations were derived for
                 the assessment of shear resistance of steel
                 fibre-reinforced concrete beams (SFRCB) using
                 multi-expression programming (MEP). The superiority of
                 MEP over conventional statistical techniques is due to
                 its ability in modelling of mechanical behaviour
                 without a need to pre-define the model structure. The
                 MEP models were developed using a comprehensive
                 database obtained through an extensive literature
                 review. New criteria were checked to verify the
                 validity of the models. A sensitivity analysis was
                 carried out and discussed. The MEP models provide good
                 estimations of the shear strength of SFRCB. The
                 developed models significantly outperform several
                 equations found in the literature.",

Genetic Programming entries for Masoud Sarveghadi A H Gandomi Hamed Bolandi A H Alavi