An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach
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- @Article{olandi2019intelligent,
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author = "Hamed Bolandi and Wolfgang Banzhaf and
Nizar Lajnef and Kaveh Barri and Amir H. Alavi",
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title = "An Intelligent Model for the Prediction of Bond
Strength of {FRP} Bars in Concrete: A Soft Computing
Approach",
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journal = "Technologies",
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year = "2019",
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volume = "7",
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number = "2",
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pages = "42",
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month = jun,
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keywords = "genetic algorithms, genetic programming, multi-gene
genetic programming, data mining, bond strength,
FRP-bar",
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ISSN = "ISSN 2227-7080",
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owner = "banzhaf",
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timestamp = "2019.06.11",
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URL = "https://www.mdpi.com/2227-7080/7/2/42/pdf",
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URL = "https://doi.org/10.3390/technologies7020042",
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DOI = "doi:10.3390/technologies7020042",
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size = "16 pages",
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abstract = "Accurate prediction of bond behaviour of fibre
reinforcement polymer (FRP) concrete has a pivotal role
in the construction industry. This paper presents a
soft computing method called multi-gene genetic
programming (MGGP) to develop an intelligent prediction
model for the bond strength of FRP bars in concrete.
The main advantage of the MGGP method over other
similar methods is that it can formulate the bond
strength by combining the capabilities of both standard
genetic programming and classical regression. A number
of parameters affecting the bond strength of FRP bars
were identified and fed into the MGGP algorithm. The
algorithm was trained using an experimental database
including 223 test results collected from the
literature. The proposed MGGP model accurately predicts
the bond strength of FRP bars in concrete. The newly
defined predictor variables were found to be efficient
in characterizing the bond strength. The derived
equation has better performance than the widely-used
American Concrete Institute (ACI) model.",
- }
Genetic Programming entries for
Hamed Bolandi
Wolfgang Banzhaf
Nizar Lajnef
Kaveh Barri
A H Alavi
Citations