A Data Mining Approach to Compressive Strength of CFRP-Confined Concrete Cylinders
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Mousavi:2010:StruEngMech2,
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author = "S. M. Mousavi and A. H. Alavi and A. H. Gandomi and
M. {Arab Esmaeili} and M. Gandomi",
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title = "A Data Mining Approach to Compressive Strength of
{CFRP}-Confined Concrete Cylinders",
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journal = "Structural Engineering and Mechanics",
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year = "2010",
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volume = "36",
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number = "6",
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pages = "759--783",
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month = dec # " 20",
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keywords = "genetic algorithms, genetic programming, multi
expression programming, CFRP-confined concrete,
compressive strength, simulated annealing,
formulation",
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ISSN = "1225-4568",
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URL = "https://mepx.github.io/papers/concrete_mep.pdf",
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URL = "http://technopress.kaist.ac.kr/?page=container&journal=sem&volume=36&num=6#",
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DOI = "doi:10.12989/sem.2010.36.6.759",
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size = "25 pages",
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abstract = "In this paper, compressive strength of carbon fibre
reinforced polymer (CFRP) confined concrete cylinders
is formulated using a hybrid method coupling genetic
programming (GP) and simulated annealing (SA), called
GP/SA, and a robust variant of GP, namely multi
expression programming (MEP). Straightforward GP/SA and
MEP-based prediction equations are derived for the
compressive strength of CFRP-wrapped concrete
cylinders. The models are constructed using two sets of
predictor variables. The first set comprises diameter
of concrete cylinder, unconfined concrete strength,
tensile strength of CFRP laminate, and total thickness
of CFRP layer. The most widely used parameters of
unconfined concrete strength and ultimate confinement
pressure are included in the second set. The models are
developed based on the experimental results obtained
from the literature. To verify the applicability of the
proposed models, they are employed to estimate the
compressive strength of parts of test results that were
not included in the modelling process. A sensitivity
analysis is carried out to determine the contributions
of the parameters affecting the compressive strength.
For more verification, a parametric study is carried
out and the trends of the results are confirmed via
some previous studies. The GP/SA and MEP models are
able to predict the ultimate compressive strength with
an acceptable level of accuracy. The proposed models
perform superior than several CFRP confinement models
found in the literature. The derived models are
particularly valuable for pre-design purposes.",
- }
Genetic Programming entries for
Seyyed Mohammad Mousavi
A H Alavi
A H Gandomi
Milad Arab Esmaeili
Mostafa Gandomi
Citations