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Record W4387270608 · doi:10.36487/acg_repo/2315_046

Risk considerations for Brazilian tailings dam closure

2023· article· en· W4387270608 on OpenAlexaboutno aff
D. A. Ritchie, Zeke Baumgardner

Bibliographic record

VenueMine closure · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsClosure (psychology)Tailings damComputer sciencePolitical scienceMaterials scienceLawMetallurgy

Abstract

fetched live from OpenAlex

The January 25, 2019 failure of the Corrego de Feijao tailings dam in Brumandinho, Brazil owned by Vale S.A. resulted in 270 lives lost along with significant environmental and cultural heritage impacts. Pursuant to this failure the Public Ministry of the State of Minas Gerais (MPMG) initiated a series of dam safety audits to enhance dam safety oversight and effect change within the Brazilian mining industry, concurrently with the Brazilian National Mining Agency (ANM) issuing legislation in February 2019 requiring the removal or stabilization and decharacterization (deregistration) of all upstream tailings dams. This paper discusses the dam closure risk considerations and experience on various dam safety audits carried out by SLR Consulting (Canada) on behalf of MPMG. The audits involved dams raised by a variety of methods including the upstream method founded on deposited tailings. In general, the focus of the technical audits included geotechnical characterization of the dam and foundation, dam design and construction stewardship, public and worker safety, and emergency preparedness, from the perspectives of both Brazilian regulations and international tailings and dam safety practice. Case studies are presented within three groupings to highlight various experiences: modifications introduced for works that had been carried out prior to commencing the audits, works designed and executed during the audit oversight, and design and planning considerations for closure works that are in progress for high-risk upstream-raised tailings dams. For all cases, the long-term risks, credible failure modes, and operational controls during construction are discussed. The Group 1 case studies highlight the importance of a holistic, long-term risk management perspective. The dam discussed was decharacterized as a mining dam prior to commencing the dam safety audits but was deemed susceptible to credible failure modes including slope instability, erosion, and spillway downcutting. In 2022 the Brazilian national mining agency updated the administration of mining dams to include a minimum of two years of monitoring after completion of the closure works. Notwithstanding, Vale is implementing improvements to the dam as part of their evolving dam safety governance practices in response to audit recommendations. Closure activities for the two dams in Group 2 involved low risk. Closure was planned and successfully completed within the period of the audits. For these dams, design decisions were made after comparing alternative closure scenarios by long-term objectives, constructability, dam safety during construction, and environmental and social considerations. Construction vibration testing was carried out to ensure construction-induced vibrations did not impact adjacent dams. The Group 3 case studies discuss design and planning decisions related to decharacterization of three upstream-raised tailings dams, two of which are at emergency levels. Prior to commencing construction dam breach inundation studies were carried out, emergency preparedness and response plans updated, and citizens in the potential inundation zone were evacuated and/or back-up or emergency containment dams were built. Construction planning included consideration of remotely-operated construction equipment, dam instrumentation (geophones, seismographs and piezometers) to detect construction-induce vibrations that could trigger tailings liquefaction, water management, and planning of staged excavations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.229
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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