A new approach for modeling of flow number of asphalt mixtures

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@Article{Alavi:2017:ACME,
  author =       "Amir H. Alavi and Hassene Hasni and Imen Zaabar and 
                 Nizar Lajnef",
  title =        "A new approach for modeling of flow number of asphalt
                 mixtures",
  journal =      "Archives of Civil and Mechanical Engineering",
  volume =       "17",
  number =       "2",
  pages =        "326--335",
  year =         "2017",
  ISSN =         "1644-9665",
  DOI =          "doi:10.1016/j.acme.2016.06.004",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1644966516300814",
  abstract =     "Flow number of asphalt-aggregate mixtures is an
                 explanatory parameter for the analysis of rutting
                 potential of asphalt mixtures. In this study, a new
                 model is proposed for the determination of flow number
                 using a robust computational intelligence technique,
                 called multi-gene genetic programming (MGGP). MGGP
                 integrates genetic programming and classical regression
                 to formulate the flow number of Marshall Specimens. A
                 reliable experimental database is used to develop the
                 proposed model. Different analyses are performed for
                 the performance evaluation of the model. On the basis
                 of a comparison study, the MGGP model performs superior
                 to the models found in the literature.",
  keywords =     "genetic algorithms, genetic programming, Asphalt
                 mixture, Flow number, Marshall mix design",
}

Genetic Programming entries for A H Alavi Hassene Hasni Imen Zaabar Nizar Lajnef

Citations