Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP
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- @Article{Nazari:2013:ESA,
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author = "Ali Nazari and F. {Pacheco Torgal}",
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title = "Modeling the compressive strength of geopolymeric
binders by gene expression programming-GEP",
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journal = "Expert Systems with Applications",
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year = "2013",
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volume = "40",
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number = "14",
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pages = "5427--5438",
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Geopolymers, Compressive
strength",
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ISSN = "0957-4174",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417413002510",
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DOI = "doi:10.1016/j.eswa.2013.04.014",
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size = "12 pages",
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abstract = "Abstract GEP has been employed in this work to model
the compressive strength of different types of
geopolymers through six different schemes. The
differences between the models were in their linking
functions, number of genes, chromosomes and head sizes.
The curing time, Ca(OH)2 content, the amount of
superplasticizer, NaOH concentration, mold type,
aluminosilicate source and H2O/Na2O molar ratio were
the seven input parameters considered in the
construction of the models to evaluate the compressive
strength of geopolymers. A total number of 399
input-target pairs were collected from the literature,
randomly divided into 299 and 100 sets and were trained
and tested, respectively. The best performance model
had 6 genes, 14 head size, 40 chromosomes and
multiplication as linking function. This was shown by
the absolute fraction of variance, the absolute
percentage error and the root mean square error. These
were of 0.9556, 2.4601 and 3.4716 for training phase,
respectively and 0.9483, 2.8456 and 3.7959 for testing
phase, respectively. However, another model with 7
genes, 12 head size, 30 chromosomes and addition as
linking function showed suitable results with the
absolute fraction of variance, the absolute percentage
error and the root mean square of 0.9547, 2.5665 and
3.4360 for training phase, respectively and 0.9466,
2.8020 and 3.8047 for testing phase, respectively.
These models showed that gene expression programming
has a strong potential for predicting the compressive
strength of different types of geopolymers in the
considered range.",
- }
Genetic Programming entries for
Ali Nazari
Fernando Pacheco Torgal
Citations