Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of froz...

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Autores principales:	Seok Yoon, Dinh-Viet Le, Gyu-Hyun Go
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/daf4bff7217d48ecafa573bdcba9914c
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spelling	oai:doaj.org-article:daf4bff7217d48ecafa573bdcba9914c2021-11-25T16:38:52ZArtificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen10.3390/app1122108342076-3417https://doaj.org/article/daf4bff7217d48ecafa573bdcba9914c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10834https://doaj.org/toc/2076-3417Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R<sup>2</sup> = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.Seok YoonDinh-Viet LeGyu-Hyun GoMDPI AGarticlefinite element methodthermal-hydro-mechanical modelparticle thermal conductivityhydraulic conductivityfrost heaveTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10834, p 10834 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Seok Yoon Dinh-Viet Le Gyu-Hyun Go Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
description	Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R<sup>2</sup> = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.
format	article
author	Seok Yoon Dinh-Viet Le Gyu-Hyun Go
author_facet	Seok Yoon Dinh-Viet Le Gyu-Hyun Go
author_sort	Seok Yoon
title	Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
title_short	Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
title_full	Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
title_fullStr	Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
title_full_unstemmed	Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
title_sort	artificial neural network-based model for prediction of frost heave behavior of silty soil specimen
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/daf4bff7217d48ecafa573bdcba9914c
work_keys_str_mv	AT seokyoon artificialneuralnetworkbasedmodelforpredictionoffrostheavebehaviorofsiltysoilspecimen AT dinhvietle artificialneuralnetworkbasedmodelforpredictionoffrostheavebehaviorofsiltysoilspecimen AT gyuhyungo artificialneuralnetworkbasedmodelforpredictionoffrostheavebehaviorofsiltysoilspecimen
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Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

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