Frozen-ground-fem: A practical and open Python 3 package for thermo-hydro-mechanical coupled modelling of soils in cold regions
Bibliographic record
Abstract
In cold regions, where soils are subjected to recurrent freeze–thaw cycles, frost heave and thaw-induced settlement are among the leading causes of ground deformation and infrastructure failure. This paper presents frozen-ground-fem , an open-source Python 3 package for modelling thermo-hydro-mechanical (THM) processes in frozen and thawing soils. The package enables one-dimensional large-strain finite element simulations that capture complex soil behaviours under freeze–thaw cycles, including temperature-dependent hydraulic conductivity, evolving void ratios, residual stresses, and settlement due to thaw consolidation. Designed with modularity and transparency in mind, frozen-ground-fem organizes code around reusable object-oriented classes for materials, elements, meshes, and boundary conditions. It supports thermal, consolidation, and coupled THM simulations using adaptive implicit time integration with iterative correction. The repository includes examples, unit tests, and detailed documentation following NumPy and PEP-8 conventions. Through benchmark scripts and interface design, this package provides a reproducible and extensible platform for researchers and engineers to simulate freeze-thaw soil deformation and assess the resilience of cold-region infrastructure under changing climatic conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".