Permeation grouting of an upstream tailing dam: a feasibility study
Bibliographic record
Abstract
The presence of potentially liquefiable deposits in tailing dams represents a serious hazard, as is now increasingly clear that liquefaction triggering may occur in unanticipated ways. For legacy dams in which this hazard is already present, intervention alternatives are sought to mitigate the associated risks of liquefaction failure. One interesting possibility is to use targeted ground improvements of tailings within the structural zone of the dams, so that they become nonliquefiable. Liquefaction remediation technologies that are relatively gentle would be preferable, as the possibility of triggering liquefaction during the ground improvement operation itself cannot be lightly discarded. Permeation grouting is a classical soil improvement technology that has been renovated with the apparition of new permeating agents, such as colloidal silica suspensions (CS). Permeation grouting of CS consists in low-pressure injections leading to CS treated soils characterized by a significantly reduced liquefaction potential. CS grout has a complex rheology that is best described by a Bingham model whose parameters change in time. This paper presents design tools that incorporate this complexity and allow both a safer and more realistic design of permeation treatments. As an application example, we study a case based on the Merriespruit tailing dam, which failed by static liquefaction. It is concluded that CS permeation could realistically offer a potential solution to reduce instability risk of some tailing storage facilities (TSF).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".