High-Precision Modeling of Uplift Capacity of Suction Caissons Using a Hybrid Computational Method
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- @Article{Alavi:2010:GeoMechEng,
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author = "Amir Hossein Alavi and Amir Hossein Gandomi and
Mehdi Mousavi and Ali Mollahasani",
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title = "High-Precision Modeling of Uplift Capacity of Suction
Caissons Using a Hybrid Computational Method",
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journal = "Geomechanics and Engineering",
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year = "2010",
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volume = "2",
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number = "4",
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pages = "253--280",
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month = dec,
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keywords = "genetic algorithms, genetic programming, suction
caissons, uplift capacity, simulated annealing,
nonlinear modelling",
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URL = "http://technopress.kaist.ac.kr/?page=container&journal=gae&volume=2&num=4",
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DOI = "doi:10.12989/gae.2010.2.4.253",
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abstract = "A new prediction model is derived for the uplift
capacity of suction caissons using a hybrid method
coupling genetic programming (GP) and simulated
annealing (SA), called GP/SA. The predictor variables
included in the analysis are the aspect ratio of
caisson, shear strength of clayey soil, load point of
application, load inclination angle, soil permeability,
and loading rate. The proposed model is developed based
on well established and widely dispersed experimental
results gathered from the literature. To verify the
applicability of the proposed model, it is employed to
estimate the uplift capacity of parts of the test
results that are not included in the modelling process.
Traditional GP and multiple regression analyses are
performed to benchmark the derived model. The external
validation of the GP/SA and GP models was further
verified using several statistical criteria recommended
by researchers. Contributions of the parameters
affecting the uplift capacity are evaluated through a
sensitivity analysis. A subsequent parametric analysis
is carried out and the obtained trends are confirmed
with some previous studies. Based on the results, the
GP/SA-based solution is effectively capable of
estimating the horizontal, vertical and inclined uplift
capacity of suction caissons. Furthermore, the GP/SA
model provides a better prediction performance than the
GP, regression and different models found in the
literature. The proposed simplified formulation can
reliably be employed for the pre-design of suction
caissons. It may be also used as a quick check on
solutions developed by more time consuming and in-depth
deterministic analyses.",
- }
Genetic Programming entries for
A H Alavi
A H Gandomi
Mehdi Mousavi
Ali Mollahasani
Citations