Bibliographic record
Abstract
In this paper, the behaviour of an idealized excavation carried out in a clay sensitive to destructuration is studied through a series of finite element analyses, employing an advanced bounding surface model developed for structured clays. Several cases are examined to investigate the influence of factors including the velocity of destructuration, damage to the soil–wall contact produced by the wall construction, and width-to-height ratio of the excavation. The case of a soil deposit insensitive to microstructural damage is also studied for comparison. Results of the numerical analyses show that the progressive dissipation of excess pore-water pressures generated during the excavation stage can damage the clay microstructure severely enough to trigger an instability phenomenon. If the clay structure deteriorates rapidly, the instability is concurrent with the dissipation of excess pore-water pressures. However, for a clay less sensitive to microstructural damage, the instability can occur towards the end of the consolidation process, or even be preceded by a deceptively stable time interval, during which small redistributions of pore-water pressure can trigger an important destructuration and collapse of the excavation. In a final part of the paper, results of the numerical analyses are used to provide indications about the most appropriate quantities to monitor to provide an effective early warning of the instability phenomenon.
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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) | 0.000 | 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 | 0.001 | 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".