Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy

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  author =       "Mehmet M. Kose and Cafer Kayadelen",
  title =        "Modeling of transfer length of prestressing strands
                 using genetic programming and neuro-fuzzy",
  journal =      "Advances in Engineering Software",
  volume =       "41",
  number =       "2",
  pages =        "315--322",
  year =         "2010",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2009.06.013",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Neuro-fuzzy,
                 Genetic expression, Prestressed concrete, Transfer
  abstract =     "In this study, the efficiency of neuro-fuzzy inference
                 system (ANFIS) and genetic expression programming (GEP)
                 in predicting the transfer length of prestressing
                 strands in prestressed concrete beams was investigated.
                 Many models suggested for the transfer length of
                 prestressing strands usually consider one or two
                 parameters and do not provide consistent accurate
                 prediction. The alternative approaches such as GEP and
                 ANFIS have been recently used to model spatially
                 complex systems. The transfer length data from various
                 researches have been collected to use in training and
                 testing ANFIS and GEP models. Six basic parameters
                 affecting the transfer length of strands were selected
                 as input parameters. These parameters are ratio of
                 strand cross-sectional area to concrete area, surface
                 condition of strands, diameter of strands, percentage
                 of debonded strands, effective prestress and concrete
                 strength at the time of measurement. Results showed
                 that the ANFIS and GEP models are capable of accurately
                 predicting the transfer lengths used in the training
                 and testing phase of the study. The GEP model results
                 better prediction compared to ANFIS model.",

Genetic Programming entries for Mehmet Metin Kose Cafer Kayadelen