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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ftmdpi:oai:mdpi.com:/2076-3417/11/22/10834/ 2023-08-20T04:09:14+02:00 Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen Seok Yoon Dinh-Viet Le Gyu-Hyun Go agris 2021-11-16 application/pdf https://doi.org/10.3390/app112210834 EN eng Multidisciplinary Digital Publishing Institute Civil Engineering https://dx.doi.org/10.3390/app112210834 https://creativecommons.org/licenses/by/4.0/ Applied Sciences; Volume 11; Issue 22; Pages: 10834 finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave Text 2021 ftmdpi https://doi.org/10.3390/app112210834 2023-08-01T03:16:15Z 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 (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets. Text permafrost MDPI Open Access Publishing Applied Sciences 11 22 10834 |
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MDPI Open Access Publishing |
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English |
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finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave |
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finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave Seok Yoon Dinh-Viet Le Gyu-Hyun Go Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen |
topic_facet |
finite element method thermal-hydro-mechanical model particle thermal conductivity hydraulic conductivity frost heave |
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 (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets. |
format |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/app112210834 |
op_coverage |
agris |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Applied Sciences; Volume 11; Issue 22; Pages: 10834 |
op_relation |
Civil Engineering https://dx.doi.org/10.3390/app112210834 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/app112210834 |
container_title |
Applied Sciences |
container_volume |
11 |
container_issue |
22 |
container_start_page |
10834 |
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1774722034732892160 |