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

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@Article{Sarveghadi:2016:NCA,
  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,
                 Prediction",
  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

Citations