A predictive equation for residual strength using a hybrid of subset selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming
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- @Article{RIAHIMADVAR:2021:GF,
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author = "Hossien Riahi-Madvar and Mahsa Gholami and
Bahram Gharabaghi and Seyed {Morteza Seyedian}",
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title = "A predictive equation for residual strength using a
hybrid of subset selection of maximum dissimilarity
method with Pareto optimal multi-gene genetic
programming",
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journal = "Geoscience Frontiers",
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volume = "12",
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number = "5",
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pages = "101222",
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year = "2021",
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ISSN = "1674-9871",
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DOI = "doi:10.1016/j.gsf.2021.101222",
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URL = "https://www.sciencedirect.com/science/article/pii/S1674987121000864",
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keywords = "genetic algorithms, genetic programming, Earth slopes,
Friction angle, Maximum dissimilarity, Multi-gene
genetic programming, Pareto-optimality, Residual
strength",
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abstract = "More accurate and reliable estimation of residual
strength friction angle (?r) of clay is crucial in many
geotechnical engineering applications, including
riverbank stability analysis, design, and assessment of
earthen dam slope stabilities. However, a general
predictive equation for ?r, with applicability in a
wide range of effective parameters, remains an
important research gap. The goal of this study is to
develop a more accurate equation for ?r using the
Pareto Optimal Multi-gene Genetic Programming (POMGGP)
approach by evaluating a comprehensive dataset of 290
experiments compiled from published literature
databases worldwide. A new framework for integrated
equation derivation proposed that hybridizes the Subset
Selection of Maximum Dissimilarity Method (SSMD) with
Multi-gene Genetic Programming (MGP) and
Pareto-optimality (PO) to find an accurate equation for
?r with wide range applicability. The final predictive
equation resulted from POMGGP modeling was assessed in
comparison with some previously published machine
learning-based equations using statistical error
analysis criteria, Taylor diagram, revised discrepancy
ratio (RDR), and scatter plots. Base on the results,
the POMGGP has the lowest uncertainty with U95 = 2.25,
when compared with Artificial Neural Network (ANN) (U95
= 2.3), Bayesian Regularization Neural Network (BRNN)
(U95 = 2.94), Levenberg-Marquardt Neural Network (LMNN)
(U95 = 3.3), and Differential Evolution Neural Network
(DENN) (U95 = 2.37). The more reliable results in
estimation of ?r derived by POMGGP with reliability
59.3percent, and resiliency 60percent in comparison
with ANN (reliability = 30.23percent, resiliency =
28.33percent), BRNN (reliability = 10.47percent,
resiliency = 10.39percent), LMNN (reliability =
19.77percent, resiliency = 20.29percent) and DENN
(reliability = 27.91percent, resiliency =
24.19percent). Besides the simplicity and ease of
application of the new POMGGP equation to a broad range
of conditions, using the uncertainty, reliability, and
resilience analysis confirmed that the derived equation
for ?r significantly outperformed other existing
machine learning methods, including the ANN, BRNN,
LMNN, and DENN equations",
- }
Genetic Programming entries for
Hossien Riahi-Madvar
Mahsa Gholami
Bahram Gharabaghi
Seyed Morteza Seyedian
Citations