Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness

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@Article{saltan:2005:IJEMS,
  author =       "Mehmet Saltan and Serdal Terzi",
  title =        "Comparative analysis of using artificial neural
                 networks (ANN) and gene expression programming (GEP) in
                 backcalculation of pavement layer thickness",
  journal =      "Indian Journal of Engineering and Materials Sciences",
  year =         "2005",
  volume =       "12",
  number =       "1",
  pages =        "42--50",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISSN =         "0971-4588",
  URL =          "http://www.niscair.res.in/ScienceCommunication/ResearchJournals/rejour/ijems/ijems2k5/ijems_feb05.asp#a42",
  URL =          "http://tef.sdu.edu.tr/~sterzi/GEP&ANN.pdf",
  abstract =     "Pavement deflection data are often used to evaluate a
                 pavement's structural condition non-destructively. It
                 is essential not only to evaluate the structural
                 integrity of an existing pavement but also to have
                 accurate information on pavement surface condition in
                 order to establish a reasonable pavement rehabilitation
                 design system. Pavement layers are characterised by
                 their elastic moduli estimated from surface deflections
                 through back calculation. Backcalculating the pavement
                 layer moduli is a well-accepted procedure for the
                 evaluation of the structural capacity of pavements. The
                 ultimate aim of the back calculation process from
                 non-destructive testing (NDT) results is to estimate
                 the pavement material properties. Using backcalculation
                 analysis, flexible pavement layer thicknesses together
                 with in-situ material properties can be back calculated
                 from the measured field data through appropriate
                 analysis techniques. In this study, artificial neural
                 networks (ANN) and gene expression programming (GEP)
                 are used in back calculating the pavement layer
                 thickness from deflections measured on the surface of
                 the flexible pavements. Experimental deflection data
                 groups from NDT are used to show the capability of the
                 ANN and GEP approaches in back calculating the pavement
                 layer thickness and compared each other. These
                 approaches can be easily and realistically performed to
                 solve the optimisation problems which do not have a
                 formulation or function about the solution.",
  notes =        "[IPC Code: Int. Cl.7 E01C 9/10]

                 CODEN : IEMSEW

                 See also 'Backcalculation of Pavement Layer Thickness
                 and Moduli Using Adaptive Neuro-fuzzy Inference System,
                 by Mehmet Saltan, Serdal Terzi, Intelligent and Soft
                 Computing in Infrastructure Systems Engineering Volume
                 259 of the series Studies in Computational Intelligence
                 pp 177-204 DOI:10.1007/978-3-642-04586-8_6",
}

Genetic Programming entries for Mehmet Saltan Serdal Terzi

Citations