Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming

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@Article{Ozbay20081831,
  author =       "Erdogan Ozbay and Mehmet Gesoglu and Erhan Guneyisi",
  title =        "Empirical modeling of fresh and hardened properties of
                 self-compacting concretes by genetic programming",
  journal =      "Construction and Building Materials",
  volume =       "22",
  number =       "8",
  pages =        "1831--1840",
  year =         "2008",
  ISSN =         "0950-0618",
  DOI =          "doi:10.1016/j.conbuildmat.2007.04.021",
  URL =          "http://www.sciencedirect.com/science/article/B6V2G-4P1276C-3/2/6cd0e931c22fa84e43fe4d289cf9b69f",
  keywords =     "genetic algorithms, genetic programming,
                 Self-compacting concrete, Fresh properties, Electrical
                 resistivity",
  abstract =     "This article introduces genetic programming (GP) as a
                 new tool for the formulations of fresh and hardened
                 properties of self-compacting concretes (SCC). There
                 are no well known explicit formulations for predicting
                 fresh and hardened properties of SCCs. Therefore, the
                 objective of the paper presented herein is to develop
                 robust formulations based on the experimental data and
                 to verify the use of GP for generating the formulations
                 for slump flow diameter, V-funnel flow time,
                 compressive strength, ultrasonic pulse velocity and
                 electrical resistivity of SCCs. To generate a database
                 for the training and testing sets, a total of 44 SCC
                 mixtures with and without mineral admixtures were cast
                 at 0.32 and 0.44 water/binder ratios. The mineral
                 admixtures used were fly ash, silica fume and
                 granulated blast furnace slag. Of all 44 concrete
                 mixtures, the training and testing sets consisted of
                 randomly selected 28 and 16 mixtures, respectively. The
                 paper showed that the GP based formulation appeared to
                 well agree with the experimental data and found to be
                 quite reliable, especially for hardened concrete
                 properties.",
}

Genetic Programming entries for Erdogan Ozbay Mehmet Gesoglu Erhan Guneyisi

Citations