Probabilistic stability analysis of a tailings dyke on presheared clayshale
Bibliographic record
Abstract
Probabilistic slope stability analysis offers an efficient framework for logical, systematic incorporation of uncertainty into slope design. The slow integration of probabilistic slope analyses into practice is attributed, among other factors, to the lack of published studies illustrating the implementation and benefits of such techniques. A spreadsheet-based, probabilistic slope analysis methodology is applied to evaluate the stability of a section of the Syncrude Tailings Dyke in Fort McMurray, Canada. The dyke is approximately 44 m high and is founded on presheared clayshale. The performance of the dyke is governed by uncertainties about material properties and pore-water pressures. Starting with field and laboratory data, this study demonstrates the techniques used in quantifying the various components of parameter uncertainty, conducting a probabilistic assessment, and estimating the probability of unsatisfactory performance. The probability of unsatisfactory performance of the dyke is estimated to be 1.6 × 10 3 . Field monitoring data indicate that the dyke performance is adequate. The study thus provides a first link between probability figures and performance. The analysis also quantifies the relative contributions of the various sources of uncertainty to the overall uncertainty in the factor of safety.Key words: probabilistic analysis, slope stability, Monte Carlo simulation, spatial variability, tailings dyke, clayshale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".