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Record W2296641741 · doi:10.82308/31179

Static and pseudo-static stability analysis of tailings storage facilities using deterministic and probabilistic methods

2013· article· en· W2296641741 on OpenAlexaboutno aff
Jenyfer Mosquera

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsProbabilistic logicTailings damEngineeringStability (learning theory)Geotechnical engineeringEnvironmental scienceCivil engineeringComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Tailings facilities are vast man-made structures designed and built for the storage and management of mill effluents throughout the life of a mining project. There are different types of tailings storage facilities (TSF) classified in accordance with the method of construction of the embankment and the mechanical properties of the tailings to be stored. The composition of tailings is determined by the mineral processing technique used to obtain the concentrate as well as the physical and chemical properties of the ore body. As a common denominator, TSFs are vulnerable to failure due to design or operational deficiencies, site-specific features, or due to random variables such as material properties, seismic events or unusual precipitation. As a result, long-term risk based stability assessment of mine wastes storage facilities is necessary.The stability analyses of TSFs are traditionally conducted using the Limit Equilibrium Method (LEM). However, it has been demonstrated that relying exclusively on this approach may not warrant full understanding of the behaviour of the TSF because the LEM neglects the stress-deformation constitutive relationships that ensure displacement compatibility. Furthermore, the intrinsic variability of tailings properties is not taken into account either because it is basically a deterministic method. In order to overcome these limitations of the LEM, new methods and techniques have been proposed for slope stability assessment. The Strength Reduction Technique (SRT) based on the Finite Element Method (FEM), for instance, has been successfully applied for this purpose. Likewise, stability assessment with the probabilistic approach has gained more and more popularity in mining engineering because it offers a comprehensive and more realistic estimation of TSFs performance. In the light of the advances in numerical modelling and geotechnical engineering applied to the mining industry, this thesis presents a stability analysis comparison between an upstream tailings storage facility (UTSF), and a water retention tailings dam (WRTD). First, the effect of embankment/tailings height increase on the overall stability is evaluated under static and pseudo-static states. Second, the effect of the phreatic surface location in the UTSF, and the embankment to core permeability ratio in the WRTD are investigated. The analyses are conducted using rigorous and simplified LEMs and the FEM - SRT. In order to take into consideration the effect of the intrinsic variability of tailings properties on stability, parametric analyses are conducted to identify the critical random variables of each TSF. Finally, the Monte Carlo Simulation (MCS), and the Point Estimate Method (PEM) are applied to recalculate the FOS and to estimate the probability of failure and reliability indices of each analysis. The results are compared against the minimum static and pseudo-static stability requirements and design guidelines applicable to mining operations in the Province of Quebec, Canada.Keywords: Tailings storage facilities (TSF), Limit Equilibrium Method (LEM), Shear Reduction Technique (SST), pseudo-static seismic coefficient, probability of failure, Point Estimate Method (PEM), Reliability Index.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.251
Teacher spread0.217 · 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.

Study designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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