Formulation of soil angle of shearing resistance using a hybrid GP and OLS method

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@Article{Mousavi:2013:EwC,
  author =       "Seyyed Mohammad Mousavi and Amir Hossein Alavi and 
                 Ali Mollahasani and Amir Hossein Gandomi and 
                 Milad {Arab Esmaeili}",
  title =        "Formulation of soil angle of shearing resistance using
                 a hybrid GP and OLS method",
  journal =      "Engineering with Computers",
  year =         "2013",
  volume =       "29",
  number =       "1",
  pages =        "37--53",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Effective
                 angle of shearing resistance, Soil physical properties,
                 Orthogonal least squares, Hybridisation",
  publisher =    "Springer",
  language =     "English",
  ISSN =         "0177-0667",
  URL =          "http://link.springer.com/article/10.1007%2Fs00366-011-0242-x",
  DOI =          "doi:10.1007/s00366-011-0242-x",
  size =         "17 pages",
  abstract =     "In the present study, a prediction model was derived
                 for the effective angle of shearing resistance (phi' )
                 of soils using a novel hybrid method coupling genetic
                 programming (GP) and orthogonal least squares algorithm
                 (OLS). The proposed nonlinear model relates phi to the
                 basic soil physical properties. A comprehensive
                 experimental database of consolidated-drained triaxial
                 tests was used to develop the model. Traditional GP and
                 least square regression analyses were performed to
                 benchmark the GP/OLS model against classical
                 approaches. Validity of the model was verified using a
                 part of laboratory data that were not involved in the
                 calibration process. The statistical measures of
                 correlation coefficient, root mean squared error, and
                 mean absolute percent error were used to evaluate the
                 performance of the models. Sensitivity and parametric
                 analyses were conducted and discussed. The GP/OLS-based
                 formula precisely estimates the phi' values for a
                 number of soil samples. The proposed model provides a
                 better prediction performance than the traditional GP
                 and regression models.",
}

Genetic Programming entries for Seyyed Mohammad Mousavi A H Alavi Ali Mollahasani A H Gandomi Milad Arab Esmaeili

Citations