New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach

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@Article{Babanajad:2017:AES,
  author =       "Saeed K. Babanajad and Amir H. Gandomi and 
                 Amir H. Alavi",
  title =        "New prediction models for concrete ultimate strength
                 under true-triaxial stress states: An evolutionary
                 approach",
  journal =      "Advances in Engineering Software",
  year =         "2017",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2017.03.011",
  URL =          "http://www.sciencedirect.com/science/article/pii/S096599781630566X",
  abstract =     "The complexity associated with the in-homogeneous
                 nature of concrete suggests the necessity of conducting
                 more in-depth behavioral analysis of this material in
                 terms of different loading configurations. Distinctive
                 feature of Gene Expression Programming (GEP) has been
                 employed to derive computer-aided prediction models for
                 the multiaxial strength of concrete under true-triaxial
                 loading. The proposed models correlate the concrete
                 true-triaxial strength ( sigma 1) to mix design
                 parameters and principal stresses ( sigma 2, sigma 3),
                 needless of conducting any time-consuming laboratory
                 experiments. A comprehensive true-triaxial database is
                 obtained from the literature to build the proposed
                 models, subsequently implemented for the verification
                 purposes. External validations as well as sensitivity
                 analysis are further carried out using several
                 statistical criteria recommended by researchers. More,
                 they demonstrate superior performance to the other
                 existing empirical and analytical models. The proposed
                 design equations can readily be used for pre-design
                 purposes or may be used as a fast check on
                 deterministic solutions.",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, Gene expression programming, Triaxial,
                 Machine learning, Computer-aided, Strength model",
}

Genetic Programming entries for Saeed K Babanajad A H Gandomi A H Alavi

Citations