Determination of the critical state of a silty sand iron tailings in triaxial extension tests using photographic correction
Bibliographic record
Abstract
Determination of the critical state of tailings is essential to the stability analysis of tailings dams, especially to assess the susceptibility to flow liquefaction failures. In contrast to the peak resistance, which can be determined in triaxial tests at relatively small strains, the critical state is often reached at higher strains. Therefore, determining the critical state in triaxial extension tests is more difficult than in compression tests because the concentration of stresses and strains in extension tests is more severe and usually inevitable, resulting in necking that may greatly affect the computed deviatoric stress and void ratio (if the test is drained). This paper presents a simple and cost-effective method of necking correction using photography. The method was used in the determination of the critical state in drained triaxial extension tests. A sample of silty sand tailings obtained from the reservoir of the Fundão dam two years prior to its collapse was used. The critical state friction angle was approximately the same in compression and extension, showing that the proposed method is promising as many authors consider that the critical state friction angle is the same in these two conditions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".