Formulation of Flow Number of Asphalt Mixes Using a Hybrid Computational Method

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Alavi:2010:CBM,
  author =       "Amir Hossein Alavi and Mahmoud Ameri and 
                 Amir Hossein Gandomi and Mohammad Reza Mirzahosseini",
  title =        "Formulation of Flow Number of Asphalt Mixes Using a
                 Hybrid Computational Method",
  journal =      "Construction and Building Materials",
  year =         "2011",
  volume =       "25",
  number =       "3",
  pages =        "1338--1355",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Asphalt
                 concrete mixture, Flow number, Simulated annealing,
                 Marshall mix design, Regression analysis",
  ISSN =         "0950-0618",
  DOI =          "doi:10.1016/j.conbuildmat.2010.09.010",
  size =         "18 pages",
  abstract =     "A high-precision model was derived to predict the flow
                 number of dense asphalt mixtures using a novel hybrid
                 method coupling genetic programming and simulated
                 annealing, called GP/SA. The proposed constitutive
                 model correlates the flow number of Marshall specimens
                 with the percentages of filler, bitumen, voids in
                 mineral aggregate, Marshall stability and flow. The
                 comprehensive experimental database used for the
                 development of the model was established upon a series
                 of uniaxial dynamic creep tests conducted in this
                 study. Generalised regression neural network and
                 multiple regression-based analyses were performed to
                 benchmark the GP/SA model. The contributions of the
                 variables affecting the flow number were evaluated
                 through a sensitivity analysis. A subsequent parametric
                 study was carried out and the trends of the results
                 were confirmed with the results of the experimental
                 study. The results indicate that the proposed GP/SA
                 model is effectively capable of evaluating the flow
                 number of asphalt mixtures. The derived model is
                 remarkably straightforward and provides an analysis
                 tool accessible to practising engineers.",
  notes =        "a School of Civil Engineering, Iran University of
                 Science and Technology, Tehran, Iran

                 b College of Civil Engineering, Tafresh University,
                 Tafresh, Iran

                 c Transportation Research Institute (TRI), Tehran,
                 Iran",
}

Genetic Programming entries for A H Alavi Mahmoud Ameri A H Gandomi Mohammad Reza Mirzahosseini

Citations