ANN-Based Prediction Model for Rutting Propensity of Asphalt Mixtures

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Mirzahosseini:2013:TRB,
  author =       "Mohammadreza Mirzahosseini and Yacoub M. Najjar and 
                 Amir Hossein Alavi and Amir Hossein Gandomi",
  title =        "{ANN}-Based Prediction Model for Rutting Propensity of
                 Asphalt Mixtures",
  booktitle =    "The 92nd Transportation Research Board (TRB) Annual
                 Meeting",
  year =         "2013",
  pages =        "Paper No. 13--2180",
  address =      "Washington, D.C., USA",
  publisher_address = "USA",
  month =        jan # " 13-17",
  organisation = "Transportation Research Board, National Research
                 Council",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, multi expression programming
                 simulated annealing, ANN",
  URL =          "http://amonline.trb.org/2013-1.264263/13-2180-1-1.291788",
  URL =          "http://assets.conferencespot.org/fileserver/file/45736/filename/2vd3kv.pdf",
  size =         "18 pages",
  abstract =     "This paper investigates the applicability of
                 artificial neural network (ANN) for the prediction of
                 the flow number of dense asphalt-aggregate mixtures.
                 Percentages of coarse aggregate, filler, bitumen, air
                 voids, voids in mineral aggregate, and Marshall
                 Quotient were employed as the predictor variables. A
                 comprehensive experimental database was used for the
                 development of the model. 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
                 model. Sensitivity and parametric analyses were
                 conducted and discussed. The ANN model accurately
                 characterises the flow number of asphalt mixtures
                 resulting in a very good prediction performance. The
                 proposed model remarkably outperforms several existing
                 prediction models for the flow number of asphalt
                 mixtures.",
  notes =        "'the entire database were compared with those provided
                 by the gene expression programming (GEP), multi
                 expression programming (MEP) (29), and hybrid GP and
                 simulated annealing (GP/SA) models.'

                 slides
                 http://assets.conferencespot.org/fileserver/file/45735/filename/39e6f1.pdf
                 'The proposed ANN model significantly outperforms the
                 existing models.' GEP MEP GP/SA

                 11 Mohammadreza Mirzahosseini* 12 Kansas State
                 University 13 Department of Civil Engineering 14
                 Manhattan, KS 66506.

                 19 Yacoub M. Najjar 20 Department of Civil Engineering
                 21 University of Mississippi 22 University, MS
                 38677.

                 27 Amir HosseinAlavi 28 Iran University of Science and
                 Technology 29 School of Civil Engineering 30 Tehran,
                 Iran

                 34 Amir HosseinGandomi 35 The University of Akron 36
                 Department of Civil Engineering 37 Akron, OH 44325.

                 ",
}

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

Citations