In situ monitoring of frost heave pressure during cross passage construction using ground-freezing method
Bibliographic record
Abstract
The artificial ground-freezing method has the dual effect of ground reinforcement and waterproof sealing, and the frozen curtain can be designed flexibly. It is widely used in the construction of cross passages for shield tunnels in soft ground with high groundwater levels. However, due to the lack of in situ monitoring data, it remains difficult to determine the frost heave pressure acting upon a tunnel. In this study, based on the use of an anti-freezing pad-type earth pressure gauge, in situ monitoring was carried out to measure the frost heave pressure acting upon tunnel segments during the construction of cross passages for the Shanghai Yangtze River Tunnel. The monitoring results show that the earth pressure acting upon the tunnel could decrease dramatically during freezing, and this kind of decrease can take place suddenly and unpredictably, which can be illustrated using the finite element method. The maximum measured frost heave pressure during freezing and cross passage excavation was approximately 0.2 MPa, which was much smaller than the predicted value. Combining the distribution of temperature in the ground and construction countermeasures, the observed phenomena are mainly related to three factors: water migration during the freezing process, the tunnel–ground interaction, and the countermeasure of pressure release holes. The tunnel showed a horizontal extension–deformation, which was consistent with the releasing frost heave pressure acting upon it.
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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 | 0.000 |
| 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".