Dynamic response of thickened tailings in shaking table testing
Bibliographic record
Abstract
In this study, an instrumented thickened tailings deposit model was designed, built and employed to evaluate the behaviour of layered thickened tailings to dynamic loading by using a shaking table equipment. The thickened tailings were deposited subsequently in three thin layers in a flexible laminar shear box mounted on top of the shaking table. Cyclic loading with a peak horizontal acceleration of 0.13 g and a frequency of 1 Hz was applied to the layered tailings deposit. Different types of sensors were placed to monitor the accelerations, displacements, volumetric water content and pore water pressures at the intermediate depth of each layer. Results indicated that the acceleration for the bottom and middle layers were similar to that of the base of the shaking table; but, this was not the case for the top layer. The measurements of vertical displacements indicated that all layers of thickened tailings experienced initially contraction and subsequently dilation during the shaking. The excess pore water pressure ratios were found to exceed unity through all layers of thickened tailings when the shaking ended. In other words, the results showed that the layered thickened tailings are susceptible to liquefaction under the considered testing conditions. It is also found that upward pore water migration to the top layer and downward pore water flow to the bottom layer occurred in the thickened tailings deposit. This water migration generated additional pore water pressure and also impacted the vertical displacement and liquefaction susceptibility of the thickened tailings material. The results of this study give a better understanding of the dynamic behaviour of thickened tailings, which is crucial for the safety of thickened tailings systems as well as sustainable mining.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".