Investigating the cyclic response of layered densified tailings deposits to drying–wetting phases: insights from shaking table tests
Bibliographic record
Abstract
Over recent decades, advances in thickening technology have enabled the adoption of highly densified tailings (HDT) (thickened and paste tailings) disposal as a viable alternative to conventional slurry tailings disposal. However, the geotechnical response and liquefaction potential of HDT subjected to drying and wetting cycles (e.g. heavy rainfall) under cyclic loading is well understood, especially in seismic regions. This study investigates the impact of drying and heavy rainfall on the behaviour and liquefaction susceptibility of layered HDT under cyclic loading, using a shaking table. Heavy rainfall was simulated to replicate extreme events in Quebec, Canada. A flexible laminar shear box equipped with various sensors and instruments (e.g. pore pressure transducers, 5TEs, cable displacement transducers) was used to simulate depositing of thin layers in the field. Results showed that excess porewater pressure (PWP) in HDT (thickened and paste tailings) deposits (initially exposed or not to drying and wetting phases) developed rapidly during shaking. However, the excess PWP ratio was found to be lower than 0.8 in the layered HDT deposits that were initially exposed to drying and wetting phases, indicating they did not liquefy. In contrast, thickened tailings not exposed to drying and wetting phases liquefied, with PWP ratios reaching 1.0. Post-shaking, the layered thickened and paste tailings deposits initially exposed to a drying and wetting phase exhibited greater resistance to liquefaction compared to those that were not, with excess PWP ratios ranging from 0.8 to 0.9. Contraction and dilation responses were seen in the layered thickened and paste tailings deposits (initially exposed to a drying and wetting [heavy rainfall] phase or not) during the shaking. The layered tailings deposits (initially exposed to a drying and wetting phase) had similar horizontal displacement response to the layered tailings deposits that were not initially exposed to a drying and wetting phase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".