Numerical study on slurry-induced fracturing pressure of cohesive soil
Bibliographic record
Abstract
The stability of the tunnel face during the slurry shield tunnelling is controlled by the slurry pressure in the slurry chamber, and excessive slurry pressure will cause fracturing on excavation face. To prevent the occurrence of slurry fracturing, it is necessary to conduct a detailed study on the slurry support pressure when the slurry fracturing occurs. In this study, the hydraulic fracturing experiment of cohesive soil samples was carried out by the self-developed hydraulic fracturing device, and the improved extended finite element method (XFEM) was used to simulate the experiment process. The influence of various factors on the fracturing pressure was analyzed, and the accuracy of the numerical simulation method was also verified. In addition, the calculation method of fracturing pressure of soft cohesive soil is obtained by fitting the influence of each factor. The results show that the improved XFEM can accurately simulate the shear failure of soft cohesive soil. The fracturing pressure increases linearly with the increase in the circumferential pressure, indicating that a higher overburden of the tunnel can help resist the initial fracturing induced by shield tunneling. The fracturing pressure increases with an increase in the unconfined compressive strength as well as the slurry viscosity. Based on the regression analysis of the numerical results, an empirical approach was proposed for estimating the slurry-induced fracturing of soil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".