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Geotechnique, Physico-chemical Behaviour and a New Erosion Model for Mine Tailings under Environmental Loading

2011· article· en· W8936883 on OpenAlexfundno aff
Rozalina S. Dimitrova

Bibliographic record

VenueJournal of Nursing Care Quality · 2011
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTailingsErosionTailings damEnvironmental scienceGeologyMining engineeringGeotechnical engineeringMetallurgyMaterials scienceGeomorphology

Abstract

fetched live from OpenAlex

Comprehensive geotechnical characterisation of mine tailings is required for the design, construction and safe operation of mine waste management facilities against the large number of potential failure risks. It is important that the characterization is carried out under the stress range and drainage conditions relevant to those encountered in the field.\nThe present research utilized mine tailings samples from a copper/nickel base-metal mine. Kaolinite and bentonite clays were added to the mine tailings in order to study the effect of clay percentage and clay mineralogy on the behaviour of saturated tailings/clay systems with varying composition. Slurries with moderate or high concentration of solids were prepared in the laboratory by mixing distilled water with mine tailings or artificial tailings/clay soils. Beds with different composition, thickness and age were sedimented from these slurries and tested under a stress range below 1 kPa, and under different degrees of drainage ranging from undrained to fully drained. The primary consolidation of the pure tailings beds was complete in approximately one hour and negligible volume changes occurred in the beds during secondary compression. The effect of adding kaolinite or bentonite to the tailings was to increase the time for primary consolidation of the mixed beds, but the rate of increase was greater when bentonite was used. The undrained shear strength of the beds was measured using an automated fall cone device at a depth interval of 1 cm below bed surface. It was found that the undrained strength increased, whereas the water content and void ratio decreased with depth within the beds. The factor controlling the undrained strength of the beds was the vertical effective stress, with the water content also having some secondary effect.\nA specially built Tilting Tank was used to measure the shear strength of the beds under drained and partially drained conditions that were simulated by varying the loading rate. Bed failure within the Tank always occurred at a plane parallel to the surface of the bed and at a depth of 0.4 to 2.5 cm. Linear drained and partially drained shear strength envelopes with zero cohesion intercept were defined, with the partially drained (total) friction angle always remaining lower than the drained (effective) friction angle. The latter varied from 35.2° for the tailings/bentonite mixtures to 40.4° for the pure tailings, depending on the percentage and mineralogy of the clay fraction. It was found that adding clay to the mine tailings generally caused a decrease in the frictional resistance of the mixtures, with the effect being more pronounced for the bentonite additive. Time for consolidation did not influence the shear strength of the tailings and tailings/kaolinite mixtures, but produced an increase of 2.1° in the frictional resistance of the tailings/bentonite mixtures.\nA critical stress for erosion as a function of depth was estimated for each bed using existing formulations for cohesive and noncohesive sediments and mixtures of both. A linear correlation between the undrained shear strength and the critical stress for erosion, with parameters dependent on the composition of the mixtures was proposed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.482

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 head0.083
GPT teacher head0.296
Teacher spread0.213 · 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 designBench or experimental
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

Citations2
Published2011
Admission routes1
Has abstractyes

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