Finite element analysis of three dimensional shallow foundation using artificial intelligence based constitutive model

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

@InProceedings{Javadi:2010:ICCCBE,
  author =       "Akbar A. Javadi and Asaad Faramarzi and 
                 Alireza Ahangar-Asr and Moura Mehravar",
  title =        "Finite element analysis of three dimensional shallow
                 foundation using artificial intelligence based
                 constitutive model",
  booktitle =    "Proceedings of the International Conference on
                 Computing in Civil and Building Engineering",
  year =         "2010",
  editor =       "W. Tizani",
  pages =        "421",
  address =      "Nottingham, UK",
  month =        "30 " # jun # "-2 " # jul,
  publisher =    "Nottingham University Press",
  note =         "Paper 211",
  keywords =     "genetic algorithms, genetic programming, constitutive
                 modelling, evolutionary computation, data mining,
                 finite element",
  isbn13 =       "978-1-907284-60-1",
  URL =          "http://www.engineering.nottingham.ac.uk/icccbe/proceedings/pdf/pf211.pdf",
  URL =          "http://www.engineering.nottingham.ac.uk/icccbe/proceedings/html/211.htm",
  size =         "6 pages",
  abstract =     "In this paper, a new approach is presented for
                 constitutive modelling of materials in finite element
                 analysis. The proposed approach provides a unified
                 framework for modelling of complex materials using
                 evolutionary polynomial regression (EPR). A procedure
                 is presented for construction of EPR-based constitutive
                 model (EPRCM) and its integration in finite element
                 procedure. The main advantage of EPRCM over
                 conventional and neural network-based constitutive
                 models is that it provides the optimum structure for
                 the material constitutive model representation as well
                 as its parameters, directly from raw experimental (or
                 field) data. It can learn nonlinear and complex
                 material behaviour without any prior assumption on the
                 constitutive relationships. The proposed algorithm
                 provides a transparent relationship for the
                 constitutive material model that can readily be
                 incorporated in a finite element model. The developed
                 EPRCM-based finite element model is used to analyse a
                 3D shallow foundation and the results are compared with
                 conventional methods. It is shown that the proposed
                 approach provides an efficient alternative to
                 conventional constitutive modelling in finite element
                 analysis.",
  notes =        "Uses GA to evolve polynomial model

                 icccbe2010
                 http://www.engineering.nottingham.ac.uk/icccbe/proceedings/html/home.htm",
}

Genetic Programming entries for Akbar A Javadi Asaad Faramarzi Alireza Ahangar-Asr Moura Mehravar

Citations