Fault tree analysis of slurry and dewatered taiings management – a framework
Bibliographic record
Abstract
Fault trees are used in reliability and risk analysis to develop the probability of occurrence of the top event, or failure mode. The top event results from a logical sequence, or combination, of lower level events using “and” and “or” logic. Probabilities for the basic events, i.e. the lowest level events identified, are calculated or estimated in order to calculate the probability for the top event. This thesis develops a framework for fault tree analysis for failure of alternative tailings depositional schemes (slurry, thickened, paste and filtered). Failure is narrowly defined as the release of tailings to the environment. The following failure modes are evaluated for each of the depositional schemes: overtopping, static liquefaction, internal erosion, static slope instability and seismic slope instability. The fault trees are representative of potential failure sequences in the industry as a whole and not on site-specific conditions. Expert elicitation methods are used to select the likelihoods of the basic events. Not all events in the fault trees are applicable to the range of depositional schemes, e.g. overtopping as a result of a large pool on slurry deposited tailings management facilities is not an event that will occur for filtered tailings. The outcome is that some of the events and parts of fault trees “fall away” as the tailings solids content increases. Apart from providing a visualization of the reduction in probability of occurrence of the top events for the failure modes, the results also provide a range of probabilities for the overall probability of failure for the range of tailings management options. The framework is used to develop a site-specific likelihood of failure of the Bafokeng tailings facility. The result demonstrates that the fault tree framework can provide useful insights in both industry-wide and site-specific tailings management facility failure likelihoods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".