Effects of material and drilling uncertainties on artificial ground freezing of cement-admixed soils
Bibliographic record
Abstract
The artificial ground freezing method can be used jointly with the deep cement mixing method during break-in and break-out processes of shield machines in a tunnel shaft. The frozen ground can fully cut off groundwater seepage, thus ensuring a watertight working platform. Cement-admixed soils can restrict frost heave and thaw-induced settlement because of the decreased permeability. Both methods can also enhance mechanical strength of the soil to enable construction to proceed. Two main sources of heterogeneity are likely to influence the freezing effect: spatial variability in in situ water content in natural soil and spatial variability in binder concentration in cement-admixed soils. Furthermore, positioning error when installing freeze pipes can also affect freezing efficiency. This study simulates in situ water content and binder concentration as Gaussian random fields, whereby variations in the thermophysical properties are estimated. Positioning error is also assessed by prescribing an incline angle in freeze pipes. The influences of those two sources of spatial variability as well as positioning error are examined with random finite-element analyses and statistical characteristics are estimated based on the results. Results are tabulated to offer practitioners a rule of thumb for estimating additional efforts needed in artificial ground freezing, accounting for variations in the thermophysical properties and positioning errors in installing freeze pipes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".