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Record W4367155207 · doi:10.36487/acg_repo/2355_19

Cemented paste backfill strength profiles for continuous pouring and liquefaction resistance

2023· article· en· W4367155207 on OpenAlexafffund
Murray Grabinsky, Benjamin Thompson, Ryan Veenstra

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsBanff CentreUniversity of TorontoGeomechanica (Canada)Cooke Aquaculture (Canada)University of Alberta
FundersBarrick Gold Corporation
KeywordsLiquefactionGeotechnical engineeringMaterials scienceGeology

Abstract

fetched live from OpenAlex

Current practice for backfilling tall stopes (e.g., long hole or Alimak) with cemented paste backfill (CPB) involves an initial plug pour to protect the barricade, followed by a main pour for the remaining stope. Many mines use higher binder content in the plug to accelerate curing time, supporting continuous pouring (i.e., no plug cure time before starting the main pour). An analytical solution to assess the required plug strength for continuous pours was recently proposed in a paper by Grabinsky et al. (2021). Similarly, various heuristics have been published suggesting a minimum Unconfined Compressive Strength will be sufficient to prevent CPB liquefaction under even the most implausible extreme loading events. However, practising mining engineers would benefit from a more straightforward design approach to assess the suitability of their backfill’s evolving strength in continuous pour and liquefaction resistance design issues. To this end, the authors have found it useful to consider the concept of a Strength Profile with depth in the plug, recognising that this Strength Profile is transient (i.e., changing with time) and must be considered for different critical stages of the plug pour and main pour. The Strength Profile design concept is explained in this paper and demonstrated using key case histories from mines where the backfill was previously monitored during continuous pouring and where the backfill materials were extensively characterised in the laboratory.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.801

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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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