Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{AWOYERA:2020:JMRT,
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author = "Paul O. Awoyera and Mehmet S. Kirgiz and
A. Viloria and D. Ovallos-Gazabon",
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title = "Estimating strength properties of geopolymer
self-compacting concrete using machine learning
techniques",
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journal = "Journal of Materials Research and Technology",
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year = "2020",
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volume = "9",
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number = "4",
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pages = "9016--9028",
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month = jul # "--" # aug,
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Artificial neural networks,
ANN, Predictor, Response, Self-Compacting concrete,
Geopolymers",
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ISSN = "2238-7854",
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DOI = "doi:10.1016/j.jmrt.2020.06.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S2238785420314095",
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size = "13 pages",
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abstract = "There has been a persistent drive for sustainable
development in the concrete industry. While there are
series of encouraging experimental research outputs,
yet the research field requires a standard framework
for the material development. In this study, the
strength characteristics of geopolymer self-compacting
concrete made by addition of mineral admixtures, have
been modelled with both genetic programming (GEP) and
the artificial neural networks (ANN) techniques. The
study adopts a 12M sodium hydroxide and sodium silicate
alkaline solution of ratio to fly ash at 0.33 for
geopolymer reaction. In addition to the conventional
material (river sand), fly ash was partially replaced
with silica fume and granulated blast furnace slag.
Various properties of the concrete, filler ability and
passing ability of fresh mixtures, and compressive,
split-tensile and flexural strength of hardened
concrete were determined. The model development
involved using raw materials and fresh mix properties
as predictors, and strength properties as response.
Results shows that the use of the admixtures enhanced
both the fresh and hardened properties of the concrete.
Both GEP and ANN methods exhibited good prediction of
the experimental data, with minimal errors. However,
GEP models can be preferred as simple equations are
developed from the process, while ANN is only a
predictor",
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notes = "Department of civil Engineering, Covenant University,
Ota, Nigeria",
- }
Genetic Programming entries for
Paul O Awoyera
Mehmet S Kirgiz
Amelec Viloria
D Ovallos-Gazabon
Citations