Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems

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@Article{Alavi:2014:NCA,
  author =       "Amir Hossein Alavi and Amir Hossein Gandomi and 
                 Hadi {Chahkandi Nejad} and Ali Mollahasani and 
                 Azadeh Rashed",
  title =        "Design equations for prediction of pressuremeter soil
                 deformation moduli utilizing expression programming
                 systems",
  journal =      "Neural Computing and Applications",
  year =         "2013",
  volume =       "23",
  number =       "6",
  pages =        "1771--1786",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Soil deformation modulus,
                 Expression programming techniques, Pressure meter test,
                 Soil physical properties",
  publisher =    "Springer-Verlag",
  ISSN =         "0941-0643",
  URL =          "http://link.springer.com/article/10.1007%2Fs00521-012-1144-6",
  DOI =          "doi:10.1007/s00521-012-1144-6",
  language =     "English",
  size =         "16 pages",
  abstract =     "Providing precise estimations of soil deformation
                 modulus is very difficult due to its dependence on many
                 factors. In this study, gene expression programming
                 (GEP) and multi-expression programming (MEP) systems
                 are presented to derive empirical equations for the
                 prediction of the pressuremeter soil deformation
                 modulus. The employed expression programming (EP)
                 systems formulate the soil deformation modulus in terms
                 of the soil physical properties. Selection of the best
                 models is on the basis of developing and controlling
                 several models with different combinations of the
                 affecting parameters. The proposed EP-based models are
                 established upon 114 pressure meter tests on different
                 soil types conducted in this study. The generalisation
                 capabilities of the models are verified using several
                 statistical criteria. Contributions of the variables
                 influencing the soil modulus are evaluated through a
                 sensitivity analysis. The GEP and MEP approaches
                 accurately characterise the soil deformation modulus
                 resulting in a very good prediction performance. The
                 result indicates that moisture content and soil dry
                 unit weight can efficiently represent the initial state
                 and consolidation history of soil for determining its
                 modulus.",
}

Genetic Programming entries for A H Alavi A H Gandomi Hadi Chahkandi Nejad Ali Mollahasani Azadeh Rashed

Citations