Empirical modeling of plate load test moduli of soil via gene expression programming
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- @Article{Mollahasani:2011:CG,
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author = "Ali Mollahasani and Amir Hossein Alavi and
Amir Hossein Gandomi",
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title = "Empirical modeling of plate load test moduli of soil
via gene expression programming",
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journal = "Computers and Geotechnics",
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year = "2011",
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volume = "38",
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number = "2",
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pages = "281--286",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Soil deformation moduli, Soil
physical properties, Nonlinear modelling",
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ISSN = "0266-352X",
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URL = "http://www.sciencedirect.com/science/article/pii/S0266352X1000162X",
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DOI = "doi:10.1016/j.compgeo.2010.11.008",
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size = "6 pages",
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abstract = "New empirical models were developed to predict the
soil deformation moduli using gene expression
programming (GEP). The principal soil deformation
parameters formulated were secant (Es) and reloading
(Er) moduli. The proposed models relate Es and Er
obtained from plate load-settlement curves to the basic
soil physical properties. The best GEP models were
selected after developing and controlling several
models with different combinations of the influencing
parameters. The experimental database used for
developing the models was established upon a series of
plate load tests conducted on different soil types at
depths of 1-24m. To verify the applicability of the
derived models, they were employed to estimate the soil
moduli of a part of test results that were not included
in the analysis. The external validation of the models
was further verified using several statistical criteria
recommended by researchers. A sensitivity analysis was
carried out to determine the contributions of the
parameters affecting Es and Er. The proposed models
give precise estimates of the soil deformation moduli.
The Es prediction model provides considerably better
results in comparison with the model developed for Er.
The simplified formulation for Es significantly
outperforms the empirical equations found in the
literature. The derived models can reliably be employed
for pre-design purposes.",
- }
Genetic Programming entries for
Ali Mollahasani
A H Alavi
A H Gandomi
Citations