Investigation on evolution law of frozen wall thickness in artificial ground freezing under seepage conditions
Bibliographic record
Abstract
Frozen wall thickness ( E) is an important index that reflects the freezing performance of artificial ground freezing (AGF). In previous design and numerical studies, the inherent spatial variability of soil properties is often neglected. Moreover, groundwater seepage can remarkably affect the freezing performance in the AGF system, while the specific relations between seepage and E remain unclear. Accordingly, this study aims to explore the evolution of frozen wall thickness under seepage conditions via a numerical model that considers the coupled thermo-hydraulic process and variations in hydrothermal properties. As two vital soil properties, spatial variability of thermal conductivity and intrinsic permeability is simulated by random field combined with Monte Carlo simulations. Based on the coupled model, the effects of seepage velocity, direction, and pipe spacing are examined by sensitivity analysis. Two indicators are introduced to quantify the influences of uncertainty in hydrothermal properties and seepage. The unfavourable scenarios (i.e., lower bound) of E from random models are tabulated and formulated via evolutionary polynomial regression for practical references. These findings contribute to an enhanced understanding of the freezing behaviours of AGF and provide a rule of thumb for predicting frozen wall thickness under complex seepage conditions and design parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".