Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Sonebi:2009:CBM,
  author =       "Mohammed Sonebi and Abdulkadir Cevik",
  title =        "Genetic programming based formulation for fresh and
                 hardened properties of self-compacting concrete
                 containing pulverised fuel ash",
  journal =      "Construction and Building Materials",
  year =         "2009",
  volume =       "23",
  pages =        "2614--2622",
  number =       "7",
  keywords =     "genetic algorithms, genetic programming, Compressive
                 strength",
  DOI =          "doi:10.1016/j.conbuildmat.2009.02.012",
  ISSN =         "0950-0618",
  URL =          "http://www.sciencedirect.com/science/article/B6V2G-4VTVJNV-1/2/bfe13c7503db42ea94f6d2d58903c660",
  URL =          "http://results.ref.ac.uk/Submissions/Output/3204773",
  abstract =     "Self-compacting concrete (SCC) flows into place and
                 around obstructions under its own weight to fill the
                 formwork completely and self-compact without any
                 segregation and blocking. Elimination of the need for
                 compaction leads to better quality concrete and
                 substantial improvement of working conditions. This
                 investigation aimed to show possible applicability of
                 genetic programming (GP) to model and formulate the
                 fresh and hardened properties of self-compacting
                 concrete (SCC) containing pulverised fuel ash (PFA)
                 based on experimental data. Twenty-six mixes were made
                 with 0.38 to 0.72 water-to-binder ratio (W/B), 183-317
                 kg/m3 of cement content, 29-261 kg/m3 of PFA, and 0 to
                 1percent of superplasticizer, by mass of powder.
                 Parameters of SCC mixes modeled by genetic programming
                 were the slump flow, JRing combined to the Orimet,
                 JRing combined to cone, and the compressive strength at
                 7, 28 and 90 days. GP is constructed of training and
                 testing data using the experimental results obtained in
                 this study. The results of genetic programming models
                 are compared with experimental results and are found to
                 be quite accurate. GP has showed a strong potential as
                 a feasible tool for modeling the fresh properties and
                 the compressive strength of SCC containing PFA and
                 produced analytical prediction of these properties as a
                 function as the mix ingredients. Results showed that
                 the GP model thus developed is not only capable of
                 accurately predicting the slump flow, JRing combined to
                 the Orimet, JRing combined to cone, and the compressive
                 strength used in the training process, but it can also
                 effectively predict the above properties for new mixes
                 designed within the practical range with the variation
                 of mix ingredients.",
  uk_research_excellence_2014 = "D - Journal article",
}

Genetic Programming entries for Mohammed Sonebi Abdulkadir Cevik

Citations