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

• 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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app112210834

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.