Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Mostafa Jalal and Ali Akbar Ramezanianpour and 
                 Ali R. Pouladkhan and Payman Tedro",
  title =        "Application of genetic programming ({GP}) and {ANFIS}
                 for strength enhancement modeling of {CFRP}-retrofitted
                 concrete cylinders",
  journal =      "Neural Computing and Applications",
  year =         "2013",
  number =       "2",
  volume =       "23",
  pages =        "455--470",
  keywords =     "genetic algorithms, genetic programming, GP, Soft
                 computing, ANFIS, Artificial neural network (ANN),
                 Concrete cylinder, CFRP composites",
  bibdate =      "2013-07-24",
  bibsource =    "DBLP,
  URL =          "",
  size =         "16 pages",
  abstract =     "Soft computing modelling of strength enhancement of
                 concrete cylinders retrofitted by carbon-fibre
                 reinforced polymer (CFRP) composites using adaptive
                 neuro-fuzzy inference system (ANFIS) and genetic
                 programming has been carried out in the present work. A
                 comparative study has also been presented using
                 artificial neural network, multiple regression and some
                 existing empirical models. The proposed models are
                 based on experimental results collected from
                 literature. The models represent the ultimate strength
                 of concrete cylinders after CFRP confinement that is in
                 terms of diameter and height of the cylindrical
                 specimen, ultimate circumferential strain in the CFRP
                 jacket, elastic modulus of CFRP, unconfined concrete
                 strength and total thickness of CFRP layer used. The
                 results obtained from different models are presented
                 and compared among which the ANFIS models are
                 considered to be the most accurate so far and quite
                 satisfactory as compared to the experimental results.",

Genetic Programming entries for Mostafa Jalal Ali Akbar Ramezanianpour Ali R Pouladkhan Payman Tedro