A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems

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@Article{Alavi:2010:ijcamieec,
  author =       "Amir Hossein Alavi and Amir Hossein Gandomi",
  title =        "A Robust Data Mining Approach for Formulation of
                 Geotechnical Engineering Systems",
  journal =      "International Journal of Computer Aided Methods in
                 Engineering-Engineering Computations",
  year =         "2011",
  volume =       "28",
  number =       "3",
  pages =        "242--274",
  email =        "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, multi expression programming,
                 Linear-based genetic programming, Data mining, Data
                 collection, Geotechnical engineering, Programming and
                 algorithm theory, Systems analysis, Formulation",
  ISSN =         "0264-4401",
  URL =          "http://www.emeraldinsight.com/journals.htm?articleid=1912293",
  DOI =          "doi:10.1108/02644401111118132",
  size =         "33 pages",
  abstract =     "Purpose- The complexity of analysis of geotechnical
                 behaviour is due to multivariable dependencies of soil
                 and rock responses. In order to cope with this complex
                 behavior, traditional forms of engineering design
                 solutions are reasonably simplified. Incorporating
                 simplifying assumptions into the development of the
                 traditional models may lead to very large errors. In
                 the present study, capabilities of promising variants
                 of genetic programming (GP), namely linear genetic
                 programming (LGP), gene expression programming (GEP)
                 and multi expression programming (MEP) are illustrated
                 by applying them to the formulation of several complex
                 geotechnical engineering
                 problems.

                 Design/methodology/approach- LGP, GEP and MEP are new
                 variants of GP that make a clear distinction between
                 the genotype and the phenotype of an individual.
                 Compared with the traditional GP, the LGP, GEP and MEP
                 techniques are more compatible with computer
                 architectures. This results in a significant speedup in
                 their execution. These methods have a great ability to
                 directly capture the knowledge contained in the
                 experimental data without making assumptions about the
                 underlying rules governing the system. This is one
                 their major advantages over most of the traditional
                 constitutive modeling methods.

                 Findings- In order to demonstrate the simulation
                 capabilities of LGP, GEP and MEP, they were applied to
                 the prediction of (i) relative crest settlement of
                 concrete-faced rockfill dams, (ii) slope stability,
                 (iii) settlement around tunnels, and (iv) soil
                 liquefaction. The results are compared with those
                 obtained by other models presented in the literature
                 and found to be more accurate. LGP has the best overall
                 behaviour for the analysis of the considered problems
                 in comparison with GEP and MEP. The simple and
                 straightforward constitutive models developed using
                 LGP, GEP and MEP provide valuable analysis tools
                 accessible to practising engineers.

                 Originality/value- The LGP, GEP and MEP approaches
                 overcome the shortcomings of different methods
                 previously presented in the literature for the analysis
                 of geotechnical engineering systems. Contrary to
                 artificial neural networks and many other soft
                 computing tools, LGP, GEP and MEP provide prediction
                 equations that can readily be used for routine design
                 practice. The constitutive models derived using these
                 methods can efficiently be incorporated into the finite
                 element or finite difference analyses as material
                 models. They may also be used as a quick check on
                 solutions developed by more time consuming and in-depth
                 deterministic analyses.",
}

Genetic Programming entries for A H Alavi A H Gandomi

Citations