Nonlinear genetic-based simulation of soil shear strength parameters

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

@Article{Mousavi:2011:JESS,
  author =       "Seyyed Mohammad Mousavi and Amir Hossein Alavi and 
                 Amir Hossein Gandomi and Ali Mollahasani",
  title =        "Nonlinear genetic-based simulation of soil shear
                 strength parameters",
  journal =      "Journal of Earth System Science",
  year =         "2011",
  volume =       "120",
  number =       "6",
  pages =        "1001--1022",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, linear-based
                 genetic programming, Soil shear strength parameters,
                 soil physical properties, prediction",
  ISSN =         "0253-4126",
  language =     "English",
  publisher =    "Springer",
  URL =          "http://link.springer.com/article/10.1007%2Fs12040-011-0119-9",
  DOI =          "doi:10.1007/s12040-011-0119-9",
  size =         "22 pages",
  abstract =     "New nonlinear solutions were developed to estimate the
                 soil shear strength parameters using linear genetic
                 programming (LGP). The soil cohesion intercept (c) and
                 angle of shearing resistance (phi) were formulated in
                 terms of the basic soil physical properties. The best
                 models were selected after developing and controlling
                 several models with different combinations of
                 influencing parameters. Comprehensive experimental
                 database used for developing the models was established
                 upon a series of unconsolidated, undrained, and
                 unsaturated triaxial tests conducted in this study.
                 Further, sensitivity and parametric analyses were
                 carried out. c and phi were found to be mostly
                 influenced by the soil unit weight and liquid limit. In
                 order to benchmark the proposed models, a multiple
                 least squares regression (MLSR) analysis was performed.
                 The validity of the models was proved on portions of
                 laboratory results that were not included in the
                 modelling process. The developed models are able to
                 effectively learn the complex relationship between the
                 soil strength parameters and their contributing
                 factors. The LGP models provide a significantly better
                 prediction performance than the regression models.",
}

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

Citations