Modeling of the angle of shearing resistance of soils using soft computing systems
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- @Article{Kayadelen200911814,
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author = "C. Kayadelen and O. Gunaydin and M. Fener and
A. Demir and A. Ozvan",
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title = "Modeling of the angle of shearing resistance of soils
using soft computing systems",
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journal = "Expert Systems with Applications",
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volume = "36",
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number = "9",
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pages = "11814--11826",
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year = "2009",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2009.04.008",
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URL = "http://www.sciencedirect.com/science/article/B6V03-4W3HX4S-1/2/c5bf935abc4eb5a42707a84dd6e518ea",
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keywords = "genetic algorithms, genetic programming, Genetic
expression programming, Neural networks, Adaptive Neuro
Fuzzy, Angle of shearing resistance of soils",
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abstract = "Precise determination of the effective angle of
shearing resistance ([phi]') value is a major concern
and an essential criterion in the design process of the
geotechnical structures, such as foundations,
embankments, roads, slopes, excavation and liner
systems for the solid waste. The experimental
determination of [phi]' is often very difficult,
expensive and requires extreme cautions and labour.
Therefore many statistical and numerical modelling
techniques have been suggested for the [phi]' value.
However they can only consider no more than one
parameter, in a simplified manner and do not provide
consistent accurate prediction of the [phi]' value.
This study explores the potential of Genetic Expression
Programming, Artificial Neural Network (ANN) and
Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the
prediction of [phi]' value of soils. The data from
consolidated-drained triaxial tests (CID) conducted in
this study and the different project in Turkey and
literature were used for training and testing of the
models. Four basic physical properties of soils that
cover the percentage of fine grained (FG), the
percentage of coarse grained (CG), liquid limit (LL)
and bulk density (BD) were presented to the models as
input parameters. The performance of models was
comprehensively evaluated some statistical criteria.
The results revealed that GEP model is fairly promising
approach for the prediction of angle of shearing
resistance of soils. The statistical performance
evaluations showed that the GEP model significantly
outperforms the ANN and ANFIS models in the sense of
training performances and prediction accuracies.",
- }
Genetic Programming entries for
Cafer Kayadelen
Osman Gunaydin
Mustafa Fener
Aydin Demir
Ali Ozvan
Citations