Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy
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- @Article{Kose2010315,
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author = "Mehmet M. Kose and Cafer Kayadelen",
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title = "Modeling of transfer length of prestressing strands
using genetic programming and neuro-fuzzy",
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journal = "Advances in Engineering Software",
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volume = "41",
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number = "2",
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pages = "315--322",
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year = "2010",
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ISSN = "0965-9978",
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DOI = "doi:10.1016/j.advengsoft.2009.06.013",
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URL = "http://www.sciencedirect.com/science/article/B6V1P-4WW7NKX-3/2/cf26e3a1767d18f4b3a93895ab93d7e8",
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keywords = "genetic algorithms, genetic programming, Neuro-fuzzy,
Genetic expression, Prestressed concrete, Transfer
length",
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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
Citations