A Data Mining Approach to Compressive Strength of CFRP Confined Concrete Cylinders

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@Article{Mousavi:2010:StruEngMech2,
  author =       "S. M. Mousavi and A. H. Alavi and A. H. Gandomi and 
                 M. {Arab Esmaeili} and M. Gandomi",
  title =        "A Data Mining Approach to Compressive Strength of CFRP
                 Confined Concrete Cylinders",
  journal =      "Structural Engineering and Mechanics",
  year =         "2010",
  volume =       "36",
  number =       "6",
  pages =        "759--783",
  month =        dec # " 20",
  keywords =     "genetic algorithms, genetic programming, multi
                 expression programming, CFRP-confined concrete,
                 compressive strength, simulated annealing,
                 formulation",
  URL =          "http://technopress.kaist.ac.kr/?page=container&journal=sem&volume=36&num=6#",
  DOI =          "doi:10.12989/sem.2010.36.6.759",
  abstract =     "In this paper, compressive strength of carbon fibre
                 reinforced polymer (CFRP) confined concrete cylinders
                 is formulated using a hybrid method coupling genetic
                 programming (GP) and simulated annealing (SA), called
                 GP/SA, and a robust variant of GP, namely multi
                 expression programming (MEP). Straightforward GP/SA and
                 MEP-based prediction equations are derived for the
                 compressive strength of CFRP-wrapped concrete
                 cylinders. The models are constructed using two sets of
                 predictor variables. The first set comprises diameter
                 of concrete cylinder, unconfined concrete strength,
                 tensile strength of CFRP laminate, and total thickness
                 of CFRP layer. The most widely used parameters of
                 unconfined concrete strength and ultimate confinement
                 pressure are included in the second set. The models are
                 developed based on the experimental results obtained
                 from the literature. To verify the applicability of the
                 proposed models, they are employed to estimate the
                 compressive strength of parts of test results that were
                 not included in the modelling process. A sensitivity
                 analysis is carried out to determine the contributions
                 of the parameters affecting the compressive strength.
                 For more verification, a parametric study is carried
                 out and the trends of the results are confirmed via
                 some previous studies. The GP/SA and MEP models are
                 able to predict the ultimate compressive strength with
                 an acceptable level of accuracy. The proposed models
                 perform superior than several CFRP confinement models
                 found in the literature. The derived models are
                 particularly valuable for pre-design purposes.",
}

Genetic Programming entries for Seyyed Mohammad Mousavi A H Alavi A H Gandomi Milad Arab Esmaeili Mostafa Gandomi

Citations