Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures

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@Article{Gandomi:2010:JMCE,
  author =       "Amir Hossein Gandomi and Amir Hossein Alavi and 
                 Mohammad Reza Mirzahosseini and 
                 Fereidoon Moghadas Nejad",
  title =        "Nonlinear Genetic-Based Models for Prediction of Flow
                 Number of Asphalt Mixtures",
  journal =      "ASCE Journal of Materials in Civil Engineering",
  year =         "2011",
  volume =       "23",
  number =       "3",
  pages =        "248--263",
  month =        mar,
  email =        "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Marshall mix design,
                 Formulation",
  URL =          "http://ascelibrary.org/mto/resource/1/jmcee7/v23/i3/p248_s1",
  DOI =          "doi:10.1061/(ASCE)MT.1943-5533.0000154",
  size =         "16 pages",
  abstract =     "Rutting has been considered as the most serious
                 distresses in flexible pavements for many years. Flow
                 number is an explanatory index for the evaluation of
                 rutting potential of asphalt mixtures. In this study, a
                 promising variant of genetic programming, namely gene
                 expression programming (GEP) is used to predict the
                 flow number of dense asphalt-aggregate mixtures. The
                 proposed constitutive models relate the flow number of
                 Marshall specimens to the coarse and fine aggregate
                 contents, percentage of air voids, percentage of voids
                 in mineral aggregate, Marshall stability and 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 were calculated to determine their
                 contributions to the flow number prediction. A multiple
                 least squares regression (MLSR) analysis was performed
                 using the same variables and data sets to benchmark the
                 GEP models. For more verification, a subsequent
                 parametric study was carried out and the trends of the
                 results were confirmed with the results of previous
                 studies. The results indicate that the proposed
                 correlations are effectively capable of evaluating the
                 flow number of asphalt mixtures. The GEP-based formulae
                 are simple, straightforward and particularly valuable
                 for providing an analysis tool accessible to practising
                 engineers.",
  notes =        "1Research Assistant, National Elites Foundation,
                 Tehran, Iran & College of Civil Engineering, Tafresh
                 University, Tafresh, Iran. 2PhD Student, School of
                 Architecture, Civil and Environmental Engineering,
                 Ecole Polytechnique Federale de Lausanne (EPFL),
                 Lausanne, Switzerland. 3Assistant Professor, College of
                 Civil Engineering, Iran University of Science &
                 Technology, Tehran, Iran. 4Assistant Professor, College
                 of Civil and Environmental Engineering, Amirkabir
                 University of Technology, Tehran, Iran.",
}

Genetic Programming entries for A H Gandomi A H Alavi Mohammad Reza Mirzahosseini Fereidoon Moghaddas Nejad

Citations