Predicting the small strain shear modulus of sands and sand-fines binary mixtures using machine learning algorithms
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- @Article{KHODKARI:2024:trgeo,
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author = "Naser Khodkari and Pouria Hamidian and
Homayoun Khodkari and Meghdad Payan and Ali Behnood",
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title = "Predicting the small strain shear modulus of sands and
sand-fines binary mixtures using machine learning
algorithms",
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journal = "Transportation Geotechnics",
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volume = "44",
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pages = "101172",
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year = "2024",
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ISSN = "2214-3912",
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DOI = "doi:10.1016/j.trgeo.2023.101172",
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URL = "https://www.sciencedirect.com/science/article/pii/S2214391223002453",
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keywords = "genetic algorithms, genetic programming, Small strain
shear modulus, Artificial neural network (ANN),
Optimization algorithm, Differential evolution (DE),
Genetic programming (GP), Sand-silt mixtures",
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abstract = "This study aims to develop several novel machine
learning (ML) evolutionary algorithms for the
prediction of small strain shear modulus (Gmax) of
clean sands and sand-fines binary mixtures. To this
end, five key features of isotropic confining pressure
(p), void ratio (e), uniformity coefficient (Cu),
particle shape descriptor (rho), and non-plastic fines
content (FC) are adopted as the inputs to artificial
neural network (ANN) models as well as genetic
programming (GP) algorithm so as to render the maximum
shear modulus of granular soils as the output.
Accordingly, a comprehensive dataset containing 1055
Gmax data points is exploited to develop ML
simulations. The validity of ML-based models in
estimating the Gmax of clean sands and sand-silt
mixtures is rigorously examined through various
statistical indices and measurement criteria. The
results show that a novel ML model using ANN-Levenberg
Marquardt (LM) in conjunction with an evolutionary
optimization method named Success History-based
Adaptive Differential Evolution with Linear population
size reduction (LSHADE) is capable of predicting Gmax
data with a very high precision rendering R2 values of
0.9833, 0.9841, 0.9802, and 0.9835 for the whole,
training, validation, and test datasets, respectively.
Meanwhile, using the well-established GP algorithm, a
new practical model is proposed to predict the Gmax of
clean sands and sand-fines mixtures containing
non-cohesive silt inclusion with R2 values of 0.9323,
0.9351, and 0.9312 for the whole, training, and test
datasets, respectively. Finally, the proposed models of
ANN-LSHADE-LM and GP are shown to be appreciably
superior, in terms of accuracy, to all commonly used
empirical correlations in the literature for Gmax
estimation",
- }
Genetic Programming entries for
Naser Khodkari
Pouria Hamidian
Homayoun Khodkari
Meghdad Payan
Ali Behnood
Citations