Bibliographic record
Abstract
This paper presents an experimental investigation of internal sand deformation around a scaled tunnel boring machine using transparent soil. Soil deformation and its control is always a critical issue, particularly in urban environments, for protecting adjacent properties and services during tunnel construction. Visualization of an internal soil deformation will improve our understanding of the influence of tunneling as most deformation measurement available are limited to ground surface settlement due to the opacity of natural soils. A new kind of transparent soil is used in this study, which is made of fused silica and a calcium bromide solution. An optical setup is developed to consist of a laser, a camera, and a computer. The laser is used to illuminate the targeted section around the scaled shield machine. A series of laser speckle images are captured during shield driving. The digital image correlation method is used to calculate the relative displacement between two consecutive images. Two model tests are performed with an overburden cover varying from once to twice the tunnel diameter. The results show that soil deformation changes with tunnel depth increases. The influence zone is changing from a rectangle over a reversed trapezoid shape in the shallower tunnel to a bell over a trapezoid shape restrained within soil mass in the deeper case. The longitudinal deformation extends to ground surface in the shallow cover case, whereas the influence zone is confined within the soil mass in the deep cover case.
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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 | 0.001 |
| 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".