Next-Generation Models for Evaluation of the Flow Number of Asphalt Mixtures

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@Article{Mirzahosseini:2015:IJGM,
  author =       "Mohammadreza Mirzahosseini and Yacoub M. Najjar and 
                 Amir H. Alavi and Amir H. Gandomi",
  title =        "Next-Generation Models for Evaluation of the Flow
                 Number of Asphalt Mixtures",
  journal =      "International Journal of Geomechanics",
  year =         "2015",
  volume =       "15",
  number =       "6",
  pages =        "04015009",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Asphalt
                 pavements, Flow number, Machine learning, Marshall mix
                 design, Prediction",
  publisher =    "American Society of Civil Engineers",
  ISSN =         "1532-3641",
  DOI =          "doi:10.1061/(ASCE)GM.1943-5622.0000483",
  abstract =     "This paper presents the development of next-generation
                 prediction models for the flow number of dense
                 asphalt-aggregate mixtures via an innovative machine
                 learning approach. New nonlinear models were developed
                 to predict the flow number using two robust machine
                 learning techniques, called linear genetic programming
                 (LGP) and artificial neural network (ANN). The flow
                 number of Marshall specimens was formulated in terms of
                 percentages of coarse aggregate, filler, bitumen, air
                 voids, voids in mineral aggregate, and Marshall
                 quotient. An experimental database containing 118 test
                 results for Marshall specimens was used for the
                 development of the models. Validity of the models was
                 verified using parts of laboratory data that were not
                 involved in the calibration process. The statistical
                 measures of coefficient of determination, coefficient
                 of efficiency, root-mean squared error, and mean
                 absolute error were used to evaluate the performance of
                 the models. Further, a multivariable least-squares
                 regression (MLSR) analysis was carried out to benchmark
                 the machine learning-based models against a classical
                 approach. Sensitivity and parametric analyses were
                 conducted and discussed. Given the results, the LGP and
                 ANN models accurately characterize the flow number of
                 asphalt mixtures. The LGP design equation reaches a
                 comparable performance with the ANN model. The proposed
                 models outperform the MLSR and other existing machine
                 learning-based models for the flow number of asphalt
                 mixtures.",
  notes =        "1Dept. of Civil Engineering, Kansas State Univ.,
                 Manhattan, KS 66506 (corresponding author). 2Dept. of
                 Civil Engineering, Univ. of Mississippi, University, MS
                 38677. 3Dept. of Civil and Environmental Engineering,
                 Michigan State Univ., East Lansing, MI 48824. 4Dept. of
                 Civil Engineering, Univ. of Akron, Akron, OH 44325.",
}

Genetic Programming entries for Mohammad Reza Mirzahosseini Yacoub Mohd Najjar A H Alavi A H Gandomi

Citations