Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches

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@Article{Baykasoglu2008,
  author =       "Adil Baykasoglu and Ahmet Oztas and Erdogan Ozbay",
  title =        "Prediction and multi-objective optimization of
                 high-strength concrete parameters via soft computing
                 approaches",
  journal =      "Expert Systems with Applications",
  year =         "2009",
  volume =       "36",
  number =       "3",
  pages =        "6145--6155",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Multiple objective
                 optimization, Meta-heuristics, Prediction,
                 High-strength concrete",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2008.07.017",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4T0WJSK-G/2/2dd2cbea4bb9a919e91f3953aecaaa06",
  ISSN =         "0957-4174",
  size =         "11 pages",
  abstract =     "The optimization of composite materials such as
                 concrete deals with the problem of selecting the values
                 of several variables which determine composition,
                 compressive stress, workability and cost etc. This
                 study presents multi-objective optimization (MOO) of
                 high-strength concretes (HSCs). One of the main
                 problems in the optimization of HSCs is to obtain
                 mathematical equations that represents concrete
                 characteristic in terms of its constitutions. In order
                 to solve this problem, a two step approach is used in
                 this study. In the first step, the prediction of HSCs
                 parameters is performed by using regression analysis,
                 neural networks and Gen Expression Programming (GEP).
                 The output of the first step is the equations that can
                 be used to predict HSCs properties (i.e. compressive
                 stress, cost and workability). In order to derive these
                 equations the data set which contains many different
                 mix proportions of HSCs is gathered from the
                 literature. In the second step, a MOO model is
                 developed by making use of the equations developed in
                 the first step. The resulting MOO model is solved by
                 using a Genetic Algorithm (GA). GA employs weighted and
                 hierarchical method in order to handle multiple
                 objectives. The performances of the prediction and
                 optimization methods are also compared in the paper.",
}

Genetic Programming entries for Adil Baykasoglu Ahmet Oztas Erdogan Ozbay

Citations