Centrifuge Modeling of Consolidation and Dynamic Loading of Fine-grained Mine Tailings
Bibliographic record
Abstract
Most experimental research on mine tailings has consisted of small scale laboratory tests and very few attempts of physical centrifuge modeling tests have been made. Geotechnical centrifuge testing allows simulation of prototype scale stresses using small scale models. Tests were performed on tailings obtained from a planned copper-gold mine and one representative test is presented herein. The mine tailings are of low plasticity with approximately 60% fine-grained material. In the field, the tailings are thickened (dewatered) and hydraulically deposited into the containment structure in layers. The objectives were to evaluate the consolidation behavior and dynamic response of a tailings stack deposited at the pumping water content (59%). As part of the study, a method of model preparation and a settlement monitoring system have been developed in order to establish the settlement profile of the model. Tailings were prepared in layers and consolidated in the centrifuge, thus allowing instrumentation of each layer before consolidation of the complete impoundment. Pore water pressures were also measured during the consolidation process. Dynamic loading was subsequently applied using the Shaking Table at the Center for Earthquake Engineering Simulation (CEES) at Rensselaer Polytechnic Institute (RPI). Accelerations and lateral displacements per layer were additionally monitored in order to evaluate the liquefaction potential of the mine tailings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".