Environmental and operational safety of tailing storage facilities: analysis of accidents, causes and technical state diagnostic methods
Bibliographic record
Abstract
A number of high-profile industrial accidents occurred at sludge and tailings storage facilities in different countries of the world are considered. The problem of ecological and technogenic danger of operating such objects, which leads to significant casualties among the civilian population, serious economic losses and harms the surrounding natural environment, is illustrated. The main causes of emergency situations have been established and analysed, it will help to reduce the risk of accidents and to minimize negative environmental consequences for similar facilities in Ukraine. The retrospective review covers the period from 1960 to 2022. During this time, about 150 cases of soil dams’ destruction in waste storage facilities were recorded. The different tendency in the frequency of accidents is noted. In particular, during the period from 1960 to 2009, there were 98 accidents with an average frequency of nearly two (1.98) per year. Over the last decade (2010-2020), the number of accidents reached 36 cases, and their frequency almost doubled to 3.6 accidents per year. Over the past two years, from the beginning of 2021 to December 2022, 10 accidents have already been registered. The vast majority of accidents during this period occurred in 34 countries of the world. The largest number of them was noted in the USA (22.4%), China (10.4%), Brazil (7.5%), Chile (6.7%), the Philippines (6.0%), Canada (5.2 %), Great Britain (4.5%) and other countries. Studies note jumps in the increase of accidents that have ten-year trends (1975, 1985, 1995, 2005). The general tendency of mass accidents since the beginning of 2015 is shown, which is substantiated by the expired terms of operation of many mining and ore enterprises (mines) and significant (exceeding normative) terms of operation of tailings storage facilities, which in some places were left without proper supervision and care. It was established that a violation of the dam slope stability (37%), an overflow of the designed capacity of the tailings storage facility (12%), seismic activity (11%), etc., are the main causes of accidents. A review of modern approaches to the management of dangerous anthropogenic objects and methods of diagnosing the technical condition of such structures was conducted. The use of a complex of organizational and technical solutions about the implementing the modern methods of assessment and control the technical condition of waste storage facilities at various levels of their operation and stages of the life cycle is proposed.
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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 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".