Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders
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- @Article{journals/nca/JalalRPT13,
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author = "Mostafa Jalal and Ali Akbar Ramezanianpour and
Ali R. Pouladkhan and Payman Tedro",
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title = "Application of genetic programming ({GP}) and {ANFIS}
for strength enhancement modeling of {CFRP}-retrofitted
concrete cylinders",
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journal = "Neural Computing and Applications",
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year = "2013",
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number = "2",
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volume = "23",
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pages = "455--470",
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note = "See Retraction Note \cite{jalal:2021:NCA}",
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keywords = "genetic algorithms, genetic programming, GP, Soft
computing, ANFIS, Artificial neural network (ANN),
Concrete cylinder, CFRP composites",
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bibdate = "2013-07-24",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca23.html#JalalRPT13",
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URL = "http://dx.doi.org/10.1007/s00521-012-0941-2",
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size = "16 pages",
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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
Citations