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Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network Cover

Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network

Studia Geotechnica et Mechanica
Volume 45 (2023): Issue s1 (December 2023)
By: Mateusz Jocz and  Marek Lefik  
Open Access
|Nov 2023

Authors

Mateusz Jocz

mateusz.jocz@dokt.p.lodz.pl

Interdisciplinary Doctoral School, Lodz University of Technology, Division of Geotechnics and Engineering Structures Department of Concrete Structures, Lodz University of Technology, Łódź

Marek Lefik

Division of Geotechnics and Engineering Structures Department of Concrete Structures, Lodz University of Technology, Łódź
DOI: https://doi.org/10.2478/sgem-2023-0023 | Journal eISSN: 2083-831X | Journal ISSN: 0137-6365
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Language: English
Page range: 405 - 415
Submitted on: Mar 3, 2023
|
Accepted on: Oct 4, 2023
|
Published on: Nov 13, 2023
Published by: Wroclaw University of Science and Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
geotechnical parameters,
Cone Penetration Test (CPTU),
liquidity index,
Long Short-Term Memory (LSTM) neural network
Related subjects:
Geosciences,
Geosciences, other,
Materials sciences,
Composites,
Porous materials,
Physics,
Mechanics and fluid dynamics

© 2023 Mateusz Jocz, Marek Lefik, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.

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