Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression

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@Article{Rezania:2010:CG,
  author =       "Mohammad Rezania and Akbar A. Javadi and 
                 Orazio Giustolisi",
  title =        "Evaluation of liquefaction potential based on CPT
                 results using evolutionary polynomial regression",
  journal =      "Computers and Geotechnics",
  year =         "2010",
  volume =       "37",
  number =       "1-2",
  pages =        "82--92",
  month =        jan # "-" # mar,
  keywords =     "genetic algorithms, genetic programming, Geotechnical
                 models, Soil liquefaction, Earthquake, Evolutionary
                 computation",
  ISSN =         "0266-352X",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0266352X09001311",
  DOI =          "doi:10.1016/j.compgeo.2009.07.006",
  size =         "11 pages",
  abstract =     "In this paper a new approach is presented, based on
                 evolutionary polynomial regression (EPR), for
                 determination of liquefaction potential of sands. EPR
                 models are developed and validated using a database of
                 170 liquefaction and non-liquefaction field case
                 histories for sandy soils based on CPT results. Three
                 models are presented to relate liquefaction potential
                 to soil geometric and geotechnical parameters as well
                 as earthquake characteristics. It is shown that the EPR
                 model is able to learn, with a very high accuracy, the
                 complex relationship between liquefaction and its
                 contributing factors in the form of a function. The
                 attained function can then be used to generalise the
                 learning to predict liquefaction potential for new
                 cases not used in the construction of the model. The
                 results of the developed EPR models are compared with a
                 conventional model as well as a number of neural
                 network-based models. It is shown that the proposed EPR
                 model provides more accurate results than the
                 conventional model and the accuracy of the EPR results
                 is better than or at least comparable to that of the
                 neural network-based models proposed in the literature.
                 The advantages of the proposed EPR model over the
                 conventional and neural network-based models are
                 highlighted.",
}

Genetic Programming entries for Mohammad Rezania Akbar A Javadi Orazio Giustolisi

Citations