State-of-the-art review of some artificial intelligence applications in pile foundations

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Shahin:2014:GF,
  author =       "Mohamed A. Shahin",
  title =        "State-of-the-art review of some artificial
                 intelligence applications in pile foundations",
  journal =      "Geoscience Frontiers",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, Pile foundations, Artificial neural
                 networks, Evolutionary polynomial regression",
  ISSN =         "1674-9871",
  DOI =          "doi:10.1016/j.gsf.2014.10.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1674987114001327",
  abstract =     "Geotechnical engineering deals with materials (e.g.
                 soil and rock) that, by their very nature, exhibit
                 varied and uncertain behaviour due to the imprecise
                 physical processes associated with the formation of
                 these materials. Modelling the behavior of such
                 materials in geotechnical engineering applications is
                 complex and sometimes beyond the ability of most
                 traditional forms of physically-based engineering
                 methods. Artificial intelligence (AI) is becoming more
                 popular and particularly amenable to modeling the
                 complex behaviour of most geotechnical engineering
                 applications because it has demonstrated superior
                 predictive ability compared to traditional methods.
                 This paper provides state-of-the-art review of some
                 selected AI techniques and their applications in pile
                 foundations, and presents the salient features
                 associated with the modelling development of these AI
                 techniques. The paper also discusses the strength and
                 limitations of the selected AI techniques compared to
                 other available modelling approaches.",
}

Genetic Programming entries for Mohamed Shahin

Citations