A Cone Penetration Test (CPT) based assessment of explosive compaction in mine tailings
Bibliographic record
Abstract
Failure of tailings dams can occur due to liquefaction of saturated and loosely deposited silt and sand sized tailings under both seismic and static conditions. The consequences in terms of loss of lives and property are severe. As tailings possess soil-like structure and grain size distribution, it is possible to use ground improvement measures typically used for soils to improve the stability of these dams. It is also possible to use densification techniques to reduce the volume of the in-place tailings to provide greater storage space. This thesis reviews the assessment of ground densification at a tailings facility in Northern Ontario at which a section of tailings dam was densified by explosive compaction (EC). The EC was complemented by surface compaction using Dynamic Compaction and Rapid Impact Compaction. Due to the nature of tailings deposition, the grain size distribution and density of materials in the dam varied considerably both laterally and with depth. This complicated the assessment of the improvement obtained. Piezometer cone penetration test data obtained before and at various times after ground treatment were reviewed to assess the range of tailings types encountered in the dam and the level of tip resistance achieved by the ground treatment. Settlement and piezometer data were also reviewed but were of insufficient quality or quantity to be useful as indicators of the degree of improvement obtained. Cone data were normalized for stress level and were sorted according to a unified soil behaviour type classification scheme previously used in soils and tailings. The soil behaviour type index, Ic, was found to be a useful indicator of tailings type. Despite the extreme variability of the deposits, it proved possible to identify the level of tip resistance achieved by the ground treatment in various material types at the site.
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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 | 0.002 | 0.002 |
| 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".