Genetic-based modeling of uplift capacity of suction caissons
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- @Article{Alavi2011,
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author = "Amir Hossein Alavi and Pejman Aminian and
Amir Hossein Gandomi and Milad {Arab Esmaeili}",
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title = "Genetic-based modeling of uplift capacity of suction
caissons",
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journal = "Expert Systems with Applications",
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volume = "38",
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number = "10",
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pages = "12608--12618",
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year = "2011",
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month = "15 " # sep,
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ISSN = "0957-4174",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417411005653",
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URL = "http://www.sciencedirect.com/science/article/B6V03-52P1KNK-4/2/f33267200d0fc51ad7a086befe3a361c",
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DOI = "doi:10.1016/j.eswa.2011.04.049",
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Suction caissons, Uplift
capacity, Formulation",
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size = "11 pages",
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abstract = "In this study, classical tree-based genetic
programming (TGP) and its recent variants, namely
linear genetic programming (LGP) and gene expression
programming (GEP) are used to develop new prediction
equations for the uplift capacity of suction caissons.
The uplift capacity is formulated in terms of several
inflecting variables. An experimental database obtained
from the literature is employed to develop the models.
Further, a conventional statistical analysis is
performed to benchmark the proposed models. Sensitivity
and parametric analyses are conducted to verify the
results. TGP, LGP and GEP are found to be effective
methods for evaluating the horizontal, vertical, and
inclined uplift capacity of suction caissons. The TGP,
LGP and GEP models reach a prediction performance
better than or comparable with the models found in the
literature.",
- }
Genetic Programming entries for
A H Alavi
Pejman Aminian
A H Gandomi
Milad Arab Esmaeili
Citations