Modeling of the angle of shearing resistance of soils using soft computing systems

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  author =       "C. Kayadelen and O. Gunaydin and M. Fener and 
                 A. Demir and A. Ozvan",
  title =        "Modeling of the angle of shearing resistance of soils
                 using soft computing systems",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "9",
  pages =        "11814--11826",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.04.008",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 expression programming, Neural networks, Adaptive Neuro
                 Fuzzy, Angle of shearing resistance of soils",
  abstract =     "Precise determination of the effective angle of
                 shearing resistance ([phi]') value is a major concern
                 and an essential criterion in the design process of the
                 geotechnical structures, such as foundations,
                 embankments, roads, slopes, excavation and liner
                 systems for the solid waste. The experimental
                 determination of [phi]' is often very difficult,
                 expensive and requires extreme cautions and labour.
                 Therefore many statistical and numerical modelling
                 techniques have been suggested for the [phi]' value.
                 However they can only consider no more than one
                 parameter, in a simplified manner and do not provide
                 consistent accurate prediction of the [phi]' value.
                 This study explores the potential of Genetic Expression
                 Programming, Artificial Neural Network (ANN) and
                 Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the
                 prediction of [phi]' value of soils. The data from
                 consolidated-drained triaxial tests (CID) conducted in
                 this study and the different project in Turkey and
                 literature were used for training and testing of the
                 models. Four basic physical properties of soils that
                 cover the percentage of fine grained (FG), the
                 percentage of coarse grained (CG), liquid limit (LL)
                 and bulk density (BD) were presented to the models as
                 input parameters. The performance of models was
                 comprehensively evaluated some statistical criteria.
                 The results revealed that GEP model is fairly promising
                 approach for the prediction of angle of shearing
                 resistance of soils. The statistical performance
                 evaluations showed that the GEP model significantly
                 outperforms the ANN and ANFIS models in the sense of
                 training performances and prediction accuracies.",

Genetic Programming entries for Cafer Kayadelen Osman Gunaydin Mustafa Fener Aydin Demir Ali Ozvan