Evolutionary-based approaches for determining the deviatoric stress of calcareous sands
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Shahnazari:2013:CG,
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author = "Habib Shahnazari and Mohammad A. Tutunchian and
Reza Rezvani and Fatemeh Valizadeh",
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title = "Evolutionary-based approaches for determining the
deviatoric stress of calcareous sands",
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journal = "Computer \& Geosciences",
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volume = "50",
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month = jan,
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pages = "84--94",
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year = "2013",
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note = "Benchmark problems, datasets and methodologies for the
computational geosciences",
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keywords = "genetic algorithms, genetic programming, Calcareous
sands, Dataset, Modeling, Triaxial experiments",
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ISSN = "0098-3004",
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DOI = "doi:10.1016/j.cageo.2012.07.006",
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URL = "http://www.sciencedirect.com/science/article/pii/S0098300412002312",
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abstract = "Many hydrocarbon reservoirs are located near oceans
which are covered by calcareous deposits. These
sediments consist mainly of the remains of marine
plants or animals, so calcareous soils can have a wide
variety of engineering properties. Due to their local
expansion and considerable differences from terrigenous
soils, the evaluation of engineering behaviours of
calcareous sediments has been a major concern for
geotechnical engineers in recent years. Deviatoric
stress is one of the most important parameters directly
affecting important shearing characteristics of soils.
In this study, a dataset of experimental triaxial tests
was gathered from two sources. First, the data of
previous experimental studies from the literature were
gathered. Then, a series of triaxial tests was
performed on calcareous sands of the Persian Gulf to
develop the dataset. This work resulted in a large
database of experimental results on the maximum
deviatoric stress of different calcareous sands. To
demonstrate the capabilities of evolutionary-based
approaches in modelling the deviatoric stress of
calcareous sands, two promising variants of genetic
programming (GP), multigene genetic programming (MGP)
and gene expression programming (GEP), were applied to
propose new predictive models. The models' input
parameters were the physical and in-situ condition
properties of soil and the output was the maximum
deviatoric stress (i.e., the axial-deviator stress).
The results of statistical analyses indicated the
robustness of these models, and a parametric study was
also conducted for further verification of the models,
in which the resulting trends were consistent with the
results of the experimental study. Finally, the
proposed models were further simplified by applying a
practical geotechnical correlation.",
- }
Genetic Programming entries for
Habib Shahnazari
Mohammad Amin Tutunchian
Reza Rezvani
Fatemeh Valizadeh
Citations