Permanent deformation analysis of asphalt mixtures using soft computing techniques

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@Article{Mirzahosseini:2011:ESA,
  author =       "Mohammad Reza Mirzahosseini and Alireza Aghaeifar and 
                 Amir Hossein Alavi and Amir Hossein Gandomi and 
                 Reza Seyednour",
  title =        "Permanent deformation analysis of asphalt mixtures
                 using soft computing techniques",
  journal =      "Expert Systems with Applications",
  year =         "2011",
  volume =       "38",
  number =       "5",
  pages =        "6081--6100",
  keywords =     "genetic algorithms, genetic programming, Multi
                 expression programming, Asphalt pavements, Rutting,
                 Artificial neural network, Marshall mix design,
                 Formulation",
  ISSN =         "0957-4174",
  URL =          "http://www.sciencedirect.com/science/article/pii/S095741741001239X",
  DOI =          "doi:10.1016/j.eswa.2010.11.002",
  size =         "20 pages",
  abstract =     "This study presents two branches of soft computing
                 techniques, namely multi expression programming (MEP)
                 and multilayer perceptron (MLP) of artificial neural
                 networks for the evaluation of rutting potential of
                 dense asphalt-aggregate mixtures. Constitutive MEP and
                 MLP-based relationships were obtained correlating the
                 flow number of Marshall specimens to the coarse and
                 fine aggregate contents, percentage of bitumen,
                 percentage of voids in mineral aggregate, Marshall
                 stability, and Marshall flow. Different correlations
                 were developed using different combinations of the
                 influencing parameters. The comprehensive experimental
                 database used for the development of the correlations
                 was established upon a series of uniaxial dynamic creep
                 tests conducted in this study. Relative importance
                 values of various predictor variables of the models
                 were calculated to determine the significance of each
                 of the variables to the flow number. A multiple least
                 squares regression (MLSR) analysis was performed to
                 benchmark the MEP and MLP models. For more
                 verification, a subsequent parametric study was also
                 carried out and the trends of the results were
                 confirmed with the experimental study results and those
                 of previous studies. The observed agreement between the
                 predicted and measured flow number values validates the
                 efficiency of the proposed correlations for the
                 assessment of the rutting potential of asphalt
                 mixtures. The MEP-based straightforward formulae are
                 much more practical for the engineering applications
                 compared with the complicated equations provided by
                 MLP.",
}

Genetic Programming entries for Mohammad Reza Mirzahosseini Alireza Aghaeifar A H Alavi A H Gandomi Reza Seyednour

Citations