Predicting the Strength Performance of Hydrated-Lime Activated Rice Husk Ash-Treated Soil Using Two Grey-Box Machine Learning Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{baghbani:2023:Geotechnics,
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author = "Abolfazl Baghbani and Amin Soltani and
Katayoon Kiany and Firas Daghistani",
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title = "Predicting the Strength Performance of {Hydrated-Lime}
Activated Rice Husk {Ash-Treated} Soil Using Two
{Grey-Box} Machine Learning Models",
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journal = "Geotechnics",
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year = "2023",
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volume = "3",
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number = "3",
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pages = "894--920",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2673-7094",
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URL = "https://www.mdpi.com/2673-7094/3/3/48",
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DOI = "doi:10.3390/geotechnics3030048",
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abstract = "Geotechnical engineering relies heavily on predicting
soil strength to ensure safe and efficient construction
projects. This paper presents a study on the accurate
prediction of soil strength properties, focusing on
hydrated-lime activated rice husk ash (HARHA) treated
soil. To achieve precise predictions, the researchers
employed two grey-box machine learning
models--classification and regression trees (CART) and
genetic programming (GP). These models introduce
innovative equations and trees that readers can readily
apply to new databases. The models were trained and
tested using a comprehensive laboratory database
consisting of seven input parameters and three output
variables. The results indicate that both the proposed
CART trees and GP equations exhibited excellent
predictive capabilities across all three output
variables--California bearing ratio (CBR), unconfined
compressive strength (UCS), and resistance value
(Rvalue) (according to the in-situ cone penetrometer
test). The GP proposed equations, in particular,
demonstrated a superior performance in predicting the
UCS and Rvalue parameters, while remaining comparable
to CART in predicting the CBR. This research highlights
the potential of integrating grey-box machine learning
models with geotechnical engineering, providing
valuable insights to enhance decision-making processes
and safety measures in future infrastructural
development projects.",
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notes = "also known as \cite{geotechnics3030048}",
- }
Genetic Programming entries for
Abolfazl Baghbani
Amin Soltani
Katayoon Kiany
Firas Daghistani
Citations