Nonlinear modelling of soil deformation modulus through LGP-based interpretation of pressuremeter test results
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Rashed2011,
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author = "Azadeh Rashed and Jafar Bolouri Bazaz and
Amir Hossein Alavi",
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title = "Nonlinear modelling of soil deformation modulus
through {LGP-based} interpretation of pressuremeter
test results",
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journal = "Engineering Applications of Artificial Intelligence",
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year = "2012",
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volume = "25",
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number = "7",
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pages = "1437--1449",
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note = "Advanced issues in Artificial Intelligence and Pattern
Recognition for Intelligent Surveillance System in
Smart Home Environment",
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ISSN = "0952-1976",
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DOI = "doi:10.1016/j.engappai.2011.11.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S0952197611002193",
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URL = "https://profdoc.um.ac.ir/articles/a/1035742.pdf",
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size = "13 pages",
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keywords = "genetic algorithms, genetic programming, Soil
deformation modulus, Pressure meter test, Soil physical
properties",
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abstract = "Soil deformation modulus is an essential parameter for
the analysis of behaviour of substructures. Despite its
importance, little attention is paid to developing
empirical models for predicting the deformation moduli
obtained from the field tests. To cope with this issue,
this paper presents the development of a new prediction
model for the pressuremeter soil deformation modulus
using a linear genetic programming (LGP) methodology.
The LGP model relates the soil secant modulus obtained
from the pressuremeter tests to the soil index
properties. The best model was 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 several pressuremeter tests
conducted on different soil types at depths of 3-40 m.
To verify the applicability of the derived model, it
was employed to estimate the soil moduli of portions of
test results that were not included in the analysis.
Further, the generalisation capability of the model was
verified via several statistical criteria. The
sensitivity of the soil deformation modulus to the
influencing variables was examined and discussed.
Moisture content and soil dry unit weight were found to
be efficient representatives of the initial state and
consolidation history of the soil for determining its
deformation modulus. The results indicate that the LGP
approach accurately characterises the soil deformation
modulus leading to a very good prediction performance.
The correlation coefficients between the experimental
and predicted soil modulus values are equal to 0.908
and 0.901 for the calibration and testing data sets,
respectively.",
- }
Genetic Programming entries for
Azadeh Rashed
Jafar Bolouri Bazaz
A H Alavi
Citations