Evaluation of liquefaction induced lateral displacements using genetic programming

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  author =       "Akbar A. Javadi and Mohammad Rezania and 
                 Mohaddeseh {Mousavi Nezhad}",
  title =        "Evaluation of liquefaction induced lateral
                 displacements using genetic programming",
  journal =      "Computers and Geotechnics",
  year =         "2006",
  volume =       "33",
  number =       "4-5",
  pages =        "222--233",
  month =        jun # "-" # jul,
  keywords =     "genetic algorithms, genetic programming, Geotechnical
                 models, Soil liquefaction, Earthquake, Evolutionary
                 computation, Evolutionary programming, Lateral
  ISSN =         "0266352X",
  DOI =          "doi:10.1016/j.compgeo.2006.05.001",
  size =         "12 pages",
  abstract =     "Determination of liquefaction induced lateral
                 displacements during earthquake is a complex
                 geotechnical engineering problem due to the complex and
                 heterogeneous nature of the soils and the participation
                 of a large number of factors involved. In this paper, a
                 new approach is presented, based on genetic programming
                 (GP), for determination of liquefaction induced lateral
                 spreading. The GP models are trained and validated
                 using a database of SPT-based case histories. Separate
                 models are presented to estimate lateral displacements
                 for free face and for gently sloping ground conditions.
                 It is shown that the GP models are able to learn, with
                 a very high accuracy, the complex relationship between
                 lateral spreading and its contributing factors in the
                 form of a function. The attained function can then be
                 used to generalise the learning to predict liquefaction
                 induced lateral spreading for new cases not used in the
                 construction of the model. The results of the developed
                 GP models are compared with those of a commonly used
                 multi linear regression (MLR) model and the advantages
                 of the proposed GP model over the conventional method
                 are highlighted.",
  notes =        "a Department of Engineering, University of Exeter,
                 Exeter EX4 4QF, Devon, UK

                 b Department of Engineering, Ferdowsi University of
                 Mashhad, Mashhad, Iran",

Genetic Programming entries for Akbar A Javadi Mohammad Rezania Mohaddeseh Mousavi-Nezhad