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Shear Strength of a Cemented Paste Backfill Submitted to High Confining Pressure

2016· article· en· W2556738307 on OpenAlexafffund
Eduardo Eiler Batista de Araújo, Dragana Simon, Fagner Alexandre Nunes de França, Osvaldo de Freitas Neto, Olavo Francisco dos Santos

Bibliographic record

VenueApplied Mechanics and Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsConsolidation (business)Curing (chemistry)Materials scienceOverburden pressureGeotechnical engineeringComposite materialGeology

Abstract

fetched live from OpenAlex

Deep mining operations require special measures in order to keep safe and economic aspects. After mine ore is extracted, voids are created and need to be filled with high-strength, low-cost materials. Cemented Paste Backfill (CPB) has recently become one of the main alternatives in filling stopes. Although numerous papers have mentioned the magnitudes of the strength of this material, its behavior under high confining pressures is still not well understood. Therefore, the purpose of this study is to increase the knowledge regarding the CPB behavior. Triaxial compression tests were performed using a Hoek Cell and Load Frame System under high confining pressures. Samples with two different binder contents were used in order to obtain the CPB strength improvement. Besides the self-weight consolidation curing method, samples were subjected to a different curing method that simulated a zero gravity condition (rotating wheel) in the first curing day to compare their mixture properties. The results suggested that both curing method and binder content have influenced the geomechanical properties of Cemented Paste Backfill. By increasing the curing time, the CPB shear strength has increased slightly, whereas specimens with higher binder content presented a significant increase in shear strength values.

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 score0.142
Threshold uncertainty score0.373

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 head0.008
GPT teacher head0.174
Teacher spread0.166 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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