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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Bibliographic Details
Published in:Applied sciences 2021-11, Vol.11 (22), p.10834
Main Authors: Yoon, Seok, Le, Dinh-Viet, Go, Gyu-Hyun
Format: Article
Language:English
Subjects:
Datasets
Energy conservation
Equilibrium
finite element method
Frost
frost heave
Frost heaving
Frozen ground
Geotechnical engineering
Heat
hydraulic conductivity
Hydraulics
Neural networks
Parameter sensitivity
particle thermal conductivity
Permafrost
Sensitivity analysis
Shear strength
Soil mechanics
Statistical analysis
Thermal conductivity
thermal-hydro-mechanical model
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112210834