Multiscale Thermo-Hydro-Mechanics of Frozen Soil: Numerical Frameworks and Constitutive Models
Bibliographic record
Abstract
This study introduces numerical frameworks for simulating the interactions within soil systems subjected to freezing and thawing processes, crucial for addressing geotechnical challenges in cold regions. By integrating robust thermo-hydro-mechanical (THM), this research offers a general understanding and specific insights into the deformation, thermal, and moisture transport behaviors of freezing-thawing soils. The first part of this study presents a soil freezing characteristic curve (SFCC) adaptable to various computational frameworks, including THM models. The SFCC, enhanced by an automatic regression scheme and a smoothing algorithm, accommodates the dynamic changes in soil properties due to phase transitions. This model effectively captures the unique behaviors of different soil types under freezing conditions, addressing key factors such as freezing temperature, compaction, and mechanical loading. Building on this foundation, the second framework employs the phase-field method (PFM) coupled with THM to model the behavior of ice-rich saturated porous media. This approach advances the field by enabling distinct representations of the mechanical behaviors of ice and soil through a diffused interface, introducing anisotropic responses as the soil undergoes freezing. By integrating a transversely isotropic plastic constitutive model for ice, this method provides a tool for capturing the phase transition processes and the resulting mechanical responses of frozen soil. The third part extends these methodologies to model thaw consolidation in permafrost regions using a THM framework combined with phase field methods. This model incorporates internal energy functions and a multiscale modified Cam-Clay model within a damage phase field framework, adept at capturing the simultaneous effects of phase change and particle rearrangement. Through validation against experimental scenarios, this model demonstrates its effectiveness in understanding the microstructural evolution and plastic softening in thaw-sensitive soils, which is vital for enhancing infrastructure resilience under thaw conditions. Together, these integrated approaches represent a leap in the modeling and simulation of geotechnical behaviors in cold regions, offering potential applications in predicting and mitigating the impacts of climate change on permafrost and other freeze-thaw affected terrains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".