Dam breach analysis of a closed facility using the Material Point Method
Bibliographic record
Abstract
The Global Industry Standard on Tailings Management (GISTM) has become a critical benchmark for tailings storage facilities (TSFs), driving mining companies to ensure compliance. This push intensified following the Brumadhino TSF failure with GISTM emphasising the need for accurate dam breach analysis (DBA) to assess potential risks, even after closure. Traditional DBA models use rheological properties to characterise the flowability of tailings. However, due to high solids concentrations in closed TSFs, these models might not be representative. Tailings behave more like solids as saturation decreases after closure. Conventional DBA models may overestimate runout distances and underestimate inundation depth, potentially leading to costly, unnecessary mitigation and misallocation of resources. The Material Point Method (MPM) is one of the techniques recommended by the Canadian Dam Association (CDA 2021) for conducting DBA. It incorporates geotechnical material properties and can be applied to closed facilities, potentially providing more realistic runout estimates. Additionally, MPM is capable of accounting for flow resistance from the embankment, unlike conventional approaches. This paper presents a case study of a closed TSF using MPM, integrating credible failure modes from the failure modes and effect analysis and residual strength parameters to trigger runout. Additionally, a probabilistic sensitivity study was carried out to account for material strength variability to capture runout behaviour even under extreme conditions. The findings demonstrate that even with unrealistically low strength values for tailings, embankment, foundation and dam materials, the predicted runout distances are limited. Insights from these models could optimise closure planning, inform more practical post-closure strategies, and improve understanding of failure impact potential. The results demonstrate that dam breach likelihood and tailings runout are highly sensitive to material strength assumptions, with full breach only occurring under extremely low-probability scenarios. Even in these cases, runout distances remained limited, highlighting the ability of the MPM approach to produce more physically representative outcomes, even under extreme conditions. These findings support a risk-informed framework for emergency preparedness and resource allocation, enhancing compliance with GISTM and promoting safer, more sustainable tailings management practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".