Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Sonebi:2009:CBM,
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author = "Mohammed Sonebi and Abdulkadir Cevik",
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title = "Genetic programming based formulation for fresh and
hardened properties of self-compacting concrete
containing pulverised fuel ash",
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journal = "Construction and Building Materials",
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year = "2009",
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volume = "23",
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pages = "2614--2622",
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number = "7",
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keywords = "genetic algorithms, genetic programming, Compressive
strength",
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DOI = "doi:10.1016/j.conbuildmat.2009.02.012",
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ISSN = "0950-0618",
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URL = "http://www.sciencedirect.com/science/article/B6V2G-4VTVJNV-1/2/bfe13c7503db42ea94f6d2d58903c660",
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URL = "http://results.ref.ac.uk/Submissions/Output/3204773",
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abstract = "Self-compacting concrete (SCC) flows into place and
around obstructions under its own weight to fill the
formwork completely and self-compact without any
segregation and blocking. Elimination of the need for
compaction leads to better quality concrete and
substantial improvement of working conditions. This
investigation aimed to show possible applicability of
genetic programming (GP) to model and formulate the
fresh and hardened properties of self-compacting
concrete (SCC) containing pulverised fuel ash (PFA)
based on experimental data. Twenty-six mixes were made
with 0.38 to 0.72 water-to-binder ratio (W/B), 183-317
kg/m3 of cement content, 29-261 kg/m3 of PFA, and 0 to
1percent of superplasticizer, by mass of powder.
Parameters of SCC mixes modeled by genetic programming
were the slump flow, JRing combined to the Orimet,
JRing combined to cone, and the compressive strength at
7, 28 and 90 days. GP is constructed of training and
testing data using the experimental results obtained in
this study. The results of genetic programming models
are compared with experimental results and are found to
be quite accurate. GP has showed a strong potential as
a feasible tool for modeling the fresh properties and
the compressive strength of SCC containing PFA and
produced analytical prediction of these properties as a
function as the mix ingredients. Results showed that
the GP model thus developed is not only capable of
accurately predicting the slump flow, JRing combined to
the Orimet, JRing combined to cone, and the compressive
strength used in the training process, but it can also
effectively predict the above properties for new mixes
designed within the practical range with the variation
of mix ingredients.",
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uk_research_excellence_2014 = "D - Journal article",
- }
Genetic Programming entries for
Mohammed Sonebi
Abdulkadir Cevik
Citations