The effect of air flow on the shear strength of soil in compressed-air tunneling
Bibliographic record
Abstract
This paper presents a method for studying the variation in shear strength of soil due to the air flow in compressed-air tunneling. The method is based on application of the shear strength theories of unsaturated soils in conjunction with a finite element model, developed by the authors, for analyzing the flow of the air through soils. The formulation of the problem, the numerical model, the design and modification of the triaxial cell and the laboratory testing program, and the results are presented and studied with particular reference to compressed-air tunneling. The results are presented and interpreted using the concepts and theory of shear strength for unsaturated soils. The results of the tests indicate the way in which the air pressure increases the stability of the ground, besides being an internal support, prior to the installation of the temporary or permanent lining. From the results of the tests, a three-dimensional failure envelope was plotted that can be used to predict the change in shear strength of the soil due to a change in the air pressure, in compressed-air tunneling. This information can be used to assess the risk of tunnel collapse and blow out. It also improves the current understanding of the interaction between the compressed-air tunneling method and the ground.Key words: compressed-air tunneling, air flow, unsaturated soils, shear strength.
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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".