Calibration of the PM4Sand model for hard-rock mine tailings based on laboratory and field testing results
Bibliographic record
Abstract
A comprehensive campaign of laboratory and field tests has been conducted to characterize the behaviour of tailings from a hard-rock mine. Cyclic triaxial, direct simple shear, and triaxial simple shear tests have shown that the contractive tailings are susceptible to liquefaction and that their behaviour is similar to loose sand despite their higher fines’ content. To model the behaviour of tailings’ impoundments in dynamic simulations, the PM4Sand constitutive model is calibrated based on the dataset presented here. Some of the model parameters are defined based on consolidation and compression tests, while the critical state line (CSL) is defined based on the results of monotonic triaxial and direct simple shear tests. The CSL is lower for tailings than for most natural sands, which is consistent with previous studies. The calibrated model can reproduce reasonably well the monotonic behaviour and provides an excellent fit of the cyclic strength curves measured in the lab at different confining pressures and density index. The results presented in this paper indicate that the PM4Sand model can be used to simulate the cyclic behaviour of low-plasticity, hard-rock tailings. The proposed procedure also provides general guidelines regarding model calibration for other types of tailings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".