Nonlinear modelling of soil deformation modulus through LGP-based interpretation of pressuremeter test results

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Rashed2011,
  author =       "Azadeh Rashed and Jafar Bolouri Bazaz and 
                 Amir Hossein Alavi",
  title =        "Nonlinear modelling of soil deformation modulus
                 through LGP-based interpretation of pressuremeter test
                 results",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2012",
  volume =       "25",
  number =       "7",
  pages =        "1437--1449",
  note =         "Advanced issues in Artificial Intelligence and Pattern
                 Recognition for Intelligent Surveillance System in
                 Smart Home Environment",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2011.11.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0952197611002193",
  size =         "13 pages",
  keywords =     "genetic algorithms, genetic programming, Soil
                 deformation modulus, Pressure meter test, Soil physical
                 properties",
  abstract =     "Soil deformation modulus is an essential parameter for
                 the analysis of behaviour of substructures. Despite its
                 importance, little attention is paid to developing
                 empirical models for predicting the deformation moduli
                 obtained from the field tests. To cope with this issue,
                 this paper presents the development of a new prediction
                 model for the pressuremeter soil deformation modulus
                 using a linear genetic programming (LGP) methodology.
                 The LGP model relates the soil secant modulus obtained
                 from the pressuremeter tests to the soil index
                 properties. The best model was selected after
                 developing and controlling several models with
                 different combinations of the influencing parameters.
                 The experimental database used for developing the
                 models was established upon several pressuremeter tests
                 conducted on different soil types at depths of 3-40 m.
                 To verify the applicability of the derived model, it
                 was employed to estimate the soil moduli of portions of
                 test results that were not included in the analysis.
                 Further, the generalisation capability of the model was
                 verified via several statistical criteria. The
                 sensitivity of the soil deformation modulus to the
                 influencing variables was examined and discussed.
                 Moisture content and soil dry unit weight were found to
                 be efficient representatives of the initial state and
                 consolidation history of the soil for determining its
                 deformation modulus. The results indicate that the LGP
                 approach accurately characterises the soil deformation
                 modulus leading to a very good prediction performance.
                 The correlation coefficients between the experimental
                 and predicted soil modulus values are equal to 0.908
                 and 0.901 for the calibration and testing data sets,
                 respectively.",
}

Genetic Programming entries for Azadeh Rashed Jafar Bolouri Bazaz A H Alavi

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