Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Alireza Ahangar-Asr and Asaad Faramarzi and 
                 Akbar A. Javadi and Orazio Giustolisi",
  title =        "Modelling mechanical behaviour of rubber concrete
                 using evolutionary polynomial regression",
  journal =      "Engineering Computation",
  year =         "2011",
  volume =       "28",
  number =       "4",
  pages =        "492--507",
  keywords =     "genetic algorithms, genetic programming, Mechanical \&
                 Materials Engineering, Concretes, Mechanical behaviour
                 of materials, Rubbers",
  ISSN =         "0264-4401",
  DOI =          "doi:10.1108/02644401111131902",
  publisher =    "Emerald Group Publishing Limited",
  abstract =     "Using discarded tyre rubber as concrete aggregate is
                 an effective solution to the environmental problems
                 associated with disposal of this waste material.
                 However, adding rubber as aggregate in concrete mixture
                 changes, the mechanical properties of concrete,
                 depending mainly on the type and amount of rubber used.
                 An appropriate model is required to describe the
                 behaviour of rubber concrete in engineering
                 applications. The purpose of this paper is to show how
                 a new evolutionary data mining technique, evolutionary
                 polynomial regression (EPR), is used to predict the
                 mechanical properties of rubber

                 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 square method is used to find feasible
                 structures and the appropriate constants for those

                 Findings Data from 70 cases of experiments on rubber
                 concrete are used for development and validation of the
                 EPR models. Three models are developed relating
                 compressive strength, splitting tensile strength, and
                 elastic modulus to a number of physical parameters that
                 are known to contribute to the mechanical behaviour of
                 rubber concrete. The most outstanding characteristic of
                 the proposed technique is that it provides a
                 transparent, structured, and accurate representation of
                 the behaviour of the material in the form of a
                 polynomial function, giving insight to the user about
                 the contributions of different parameters involved. The
                 proposed model shows excellent agreement with
                 experimental results, and provides an efficient method
                 for estimation of mechanical properties of rubber

                 Originality/value In this paper, a new evolutionary
                 data mining approach is presented for the analysis of
                 mechanical behaviour of rubber concrete. The new
                 approach overcomes the shortcomings of the traditional
                 and artificial neural network-based methods presented
                 in the literature for the analysis of slopes. 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
  notes =        "Research paper.

                 Computational Geomechanics Group, College of
                 Engineering, Mathematics and Physical Sciences,
                 University of Exeter, Exeter, UK Civil and
                 Environmental Engineering Department, Faculty of
                 Engineering, Technical University of Bari, Taranto,

Genetic Programming entries for Alireza Ahangar-Asr Asaad Faramarzi Akbar A Javadi Orazio Giustolisi