Static liquefaction as a possible explanation for the Merriespruit tailings dam failure
Bibliographic record
Abstract
In 1994 the Merriespruit gold tailings dam in South Africa failed, resulting in 17 deaths. The post-failure investigation provided no explanation as to why the catastrophic flow failure, which contradicted all previous experiences of failures of gold tailings dams in South Africa, occurred. The documented history of the dam describes insufficient freeboard provision and often poor pool control, which is argued to have resulted in some areas of the dam having high in situ void ratios. Some of the undrained triaxial tests carried out on specimens obtained from zones adjacent to the failure scar exhibited nondilative behaviour. Laboratory triaxial tests that were conducted on reconstituted specimens and are reported in a companion paper defined a series of steady state lines that were dependent on the particle-size distribution of the tailings. Void ratios obtained from undisturbed samples taken during the post-failure investigation are compared with these steady state lines and it is shown that an appreciable percentage of the specimens were likely to have been contractant. The inference drawn is that a large volume of tailings was in a metastable state in situ and overtopping and erosion of the impoundment wall exposed this material, resulting in static liquefaction of the tailings and a consequent flow failure.Key words: static liquefaction, gold tailings, Merriespruit, failure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".