Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques
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- @Article{onyelowe:2022:Designs,
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author = "Kennedy C. Onyelowe and Jagan Jayabalan and
Ahmed M. Ebid and Pijush Samui and Rahul Pratap Singh and
Atefeh Soleymani and Hashem Jahangir",
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title = "Evaluation of the Compressive Strength of
{CFRP-Wrapped} Circular Concrete Columns Using
Artificial Intelligence Techniques",
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journal = "Designs",
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year = "2022",
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volume = "6",
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number = "6",
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pages = "Article No. 112",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2411-9660",
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URL = "https://www.mdpi.com/2411-9660/6/6/112",
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DOI = "doi:10.3390/designs6060112",
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abstract = "The wrapping of concrete structures with fiber
polymers has been an essential part of concrete
technology aimed at the improvement of concrete
performance indices during the construction and
lifelong usage of the structures. In this paper, a
universal representative database was collected from
multiple literature materials on the effect of
different fiber-reinforced polymers on the confined
compressive strength of wrapped concrete columns (Fcc).
The collected data show that the Fcc value depends on
the FRP thickness (t), tensile strength (Ftf), and
elastic modulus (Ef), in addition to the column
diameter (d) and the confined compressive strength of
concrete (Fco). Five AI techniques were applied on the
collected database, namely genetic programming (GP),
three artificial neural networks (ANN) trained using
three different algorithms, “back Propagation BP,
gradually reduced gradient GRG and genetic algorithm
GA”, and evolutionary polynomial regression
(EPR). The results of the five developed predictive
models show that (t) and Ftf have a major impact on the
Fcc value, which presents the effect of confinement
stress (t. Ftf/d) on the confined compressive strength
(Fcc). Comparing the predicted values with the
experimental ones showed that the GP model is the least
accurate one, and the EPR model is the next least
accurate, while the three ANN models have almost the
same level of high accuracy, with an average error
percentage of 5.8percent and a coefficient of
determination R2 of 0.961. The ANN model is more
accurate than the EPR and GP predictive models, but
they are suitable for manual calculation because they
are closed-form equations.",
-
notes = "also known as \cite{designs6060112}",
- }
Genetic Programming entries for
Kennedy C Onyelowe
Jagan Jayabalan
Ahmed M Ebid
Pijush Samui
Rahul Pratap Singh
Atefeh Soleymani
Hashem Jahangir
Citations