An evolutionary-based data mining technique for assessment of civil engineering systems

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@Article{Rezania:2008:EC,
  author =       "Mohammad Rezania and Akbar A. Javadi and 
                 Orazio Giustolisi",
  title =        "An evolutionary-based data mining technique for
                 assessment of civil engineering systems",
  journal =      "Engineering Computations",
  year =         "2008",
  volume =       "25",
  number =       "6",
  pages =        "500--517",
  keywords =     "genetic algorithms, genetic programming, Civil
                 engineering, Data mining, Modelling, Polynomials",
  ISSN =         "0264-4401",
  DOI =          "doi:10.1108/02644400810891526",
  abstract =     "Purpose

                 Analysis of many civil engineering phenomena is a
                 complex problem due to the participation of a large
                 number of factors involved. Traditional methods usually
                 suffer from a lack of physical understanding.
                 Furthermore, the simplifying assumptions that are
                 usually made in the development of the traditional
                 methods may, in some cases, lead to very large errors.
                 The purpose of this paper is to present a new method,
                 based on evolutionary polynomial regression (EPR) for
                 capturing nonlinear interaction between various
                 parameters of civil engineering
                 systems.

                 Design/methodology/approach

                 EPR is a data-driven method based on evolutionary
                 computing, aimed to search for polynomial structures
                 representing a system. In this technique, a combination
                 of the genetic algorithm and the least-squares method
                 is used to find feasible structures and the appropriate
                 constants for those structures.

                 Findings

                 Capabilities of the EPR methodology are illustrated by
                 application to two complex practical civil engineering
                 problems including evaluation of uplift capacity of
                 suction caissons and shear strength of reinforced
                 concrete deep beams. The results show that the proposed
                 EPR model provides a significant improvement over the
                 existing models. The EPR models generate a transparent
                 and structured representation of the system. For design
                 purposes, the EPR models, presented in this study, are
                 simple to use and provide results that are more
                 accurate than the existing
                 methods.

                 Originality/value

                 In this paper, a new evolutionary data mining approach
                 is presented for the analysis of complex civil
                 engineering problems. The new approach overcomes the
                 shortcomings of the traditional and artificial neural
                 network-based methods presented in the literature for
                 the analysis of civil engineering systems. EPR provides
                 a viable tool to find a structured representation of
                 the system, which allows the user to gain additional
                 information on how the system performs",
  notes =        "Mohammad Rezania, (Computational Geomechanics Group,
                 School of Engineering, Computing and Mathematics,
                 University of Exeter, Exeter, UK), Akbar A. Javadi,
                 (Computational Geomechanics Group, School of
                 Engineering, Computing and Mathematics, University of
                 Exeter, Exeter, UK), Orazio Giustolisi, (Department of
                 Civil and Environmental Engineering, Technical
                 University of Bari, Bari, Italy Engineering Faculty of
                 Taranto, Taranto, Italy)",
}

Genetic Programming entries for Mohammad Rezania Akbar A Javadi Orazio Giustolisi

Citations