A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

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@Article{journals/nca/GandomiA12a,
  author =       "Amir Hossein Gandomi and Amir Hossein Alavi",
  title =        "A new multi-gene genetic programming approach to
                 non-linear system modeling. Part {II}: geotechnical and
                 earthquake engineering problems",
  journal =      "Neural Computing and Applications",
  year =         "2012",
  number =       "1",
  volume =       "21",
  pages =        "189--201",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0941-0643",
  DOI =          "doi:10.1007/s00521-011-0735-y",
  size =         "13 pages",
  abstract =     "Complexity of analysis of geotechnical behaviour is
                 due to multivariable dependencies of soil and rock
                 responses. In order to cope with this complex
                 behaviour, traditional forms of engineering design
                 solutions are reasonably simplified. Incorporating
                 simplifying assumptions into the development of the
                 traditional methods may lead to very large errors. This
                 paper presents an endeavour to exploit a robust
                 multi-gene genetic programming (MGGP) method for the
                 analysis of geotechnical and earthquake engineering
                 systems. MGGP is a modified genetic programming
                 approach for model structure selection combined with a
                 classical technique for parameter estimation. To
                 justify the abilities of MGGP, it is systematically
                 employed to formulate the complex geotechnical
                 engineering problems. Different classes of the problems
                 analysed include the assessment of (i) undrained
                 lateral load capacity of piles, (ii) undrained side
                 resistance alpha factor for drilled shafts, (iii)
                 settlement around tunnels, and (iv) soil liquefaction.
                 The validity of the derived models is tested for a part
                 of test results beyond the training data domain.
                 Numerical examples show the superb accuracy,
                 efficiency, and great potential of MGGP. Contrary to
                 artificial neural networks and many other soft
                 computing tools, MGGP provides constitutive prediction
                 equations. The MGG-based solutions are particularly
                 valuable for pre-design practices.",
  notes =        "See \cite{journals/nca/GandomiA12}",
  affiliation =  "Department of Civil Engineering, The University of
                 Akron, Akron, OH 44325-3905, USA",
  bibdate =      "2012-01-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca21.html#GandomiA12a",
}

Genetic Programming entries for A H Gandomi A H Alavi

Citations