Comparison of genetic programming with conventional methods for fatigue life modeling of FRP composite materials

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@Article{Vassilopoulos20081634,
  author =       "Anastasios P. Vassilopoulos and 
                 Efstratios F. Georgopoulos and Thomas Keller",
  title =        "Comparison of genetic programming with conventional
                 methods for fatigue life modeling of FRP composite
                 materials",
  journal =      "International Journal of Fatigue",
  volume =       "30",
  number =       "9",
  pages =        "1634--1645",
  year =         "2008",
  ISSN =         "0142-1123",
  DOI =          "doi:10.1016/j.ijfatigue.2007.11.007",
  URL =          "http://www.sciencedirect.com/science/article/B6V35-4R6B2M8-1/2/1d030f0598bc4659ba08b55c82930264",
  keywords =     "genetic algorithms, genetic programming, Fatigue, Life
                 prediction, S-N curve",
  abstract =     "Genetic programming is used in this paper for modeling
                 the fatigue life of several fiber-reinforced composite
                 material systems. It is shown that if the genetic
                 programming tool is adequately trained, it can produce
                 theoretical predictions that compare favorably with
                 corresponding predictions by other, conventional
                 methods for the interpretation of fatigue data. For the
                 comparison of results, curves produced by the genetic
                 programming tool are plotted together with curves
                 produced by three other commonly used methods for the
                 analysis of composite material fatigue data: linear
                 regression, Whitney's Weibull statistics and
                 Sendeckyj's wear-out model. The modeling accuracy of
                 this computational technique, whose application for
                 this purpose is novel, is very high. The proposed
                 modeling technique presents certain advantages compared
                 to conventional methods. The new technique is a
                 stochastic process that leads straight to a multi-slope
                 S-N curve that follows the trend of the experimental
                 data, without the need for any assumptions.",
}

Genetic Programming entries for Anastasios P Vassilopoulos Efstratios F Georgopoulos Thomas Keller

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