Estimating uplift capacity of suction caissons in soft clay: A hybrid computational approach based on model tree and GP
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- @Article{DERAKHSHANI:2017:OE,
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author = "Ali Derakhshani",
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title = "Estimating uplift capacity of suction caissons in soft
clay: A hybrid computational approach based on model
tree and GP",
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journal = "Ocean Engineering",
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volume = "146",
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pages = "1--8",
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year = "2017",
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keywords = "genetic algorithms, genetic programming, Suction
caisson, Uplift capacity, Formulation, Hybrid
intelligent approach, M5-GP method",
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ISSN = "0029-8018",
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DOI = "doi:10.1016/j.oceaneng.2017.09.025",
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URL = "http://www.sciencedirect.com/science/article/pii/S0029801817305449",
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abstract = "Stability of suction caissons used as foundations or
anchors of offshore structures is a critical challenge
in marine structures engineering. To this end, many
studies have been conducted including those concentrate
on implementing computational intelligence methods to
model the response of suction caissons under loading.
In this regard, this paper aims at formulating uplift
capacity of suction caissons using a hybrid artificial
intelligence computational tool based on model tree
(M5) and genetic programming (GP), called M5-GP. The
formulae are developed in terms of several governing
parameters using a reliable experimental database from
the literature. The results show that the M5-GP based
relationships are able to predict the uplift capacity
of suction caissons precisely. Furthermore, to consider
the safety in the design process, probabilistic
equations are also given for various risk levels. The
new formulas compare favorably with the existing
relationships in the literature regarding prediction
performance. In addition, the simplified formulation is
compact, easy to use and physically sound. Therefore,
it is especially appropriate to be used in design
practice",
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keywords = "genetic algorithms, genetic programming, Suction
caisson, Uplift capacity, Formulation, Hybrid
intelligent approach, M5-GP method",
- }
Genetic Programming entries for
Ali Derakhshani
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