Multi-stage creep behavior of frozen granular soils: experimental evidence and constitutive modeling
Bibliographic record
Abstract
The significance of ground freezing is becoming ever more germane as the design of new urban tunneling systems requires more complex geometries and higher bearing capacities, which are limited with conventional construction methods. Ground freezing is an advanced construction technique to make the water-saturated subsoil impermeable and temporarily increase its strength and stiffness. This study reports experimental investigations consisting of single-stage and multi-stage creep tests under uniaxial loading. The comparison of the different loading types reveals the influence of the stress–strain history on the rate- and temperature-dependent behavior of frozen granular soils. We extend the constitutive model for frozen soils proposed by Cudmani et al. (2022, Géotechnique, doi:10.1680/jgeot.21.00012) to consider stepwise loading and creep by coupling creep time with stress–strain history. Moreover, we simulate element tests and compare the simulations with our own experimental data as well as data from the literature to achieve the first step in validating the extended model. The good agreement of the numerical and experimental results confirms the constitutive model’s ability to capture the main features of the complex mechanical behavior of frozen granular soils for single-stage as well as multi-stage loading under constant temperatures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".