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Record W4322499677 · doi:10.3390/eng4010044

Mechanical Characterization of Cemented Paste Backfill

2023· article· en· W4322499677 on OpenAlexafffund
Andrew Pan, Murray Grabinsky

Bibliographic record

VenueEng—Advances in Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoBarrick Gold CorporationNatural Sciences and Engineering Research Council of CanadaPortland State University
KeywordsUltimate tensile strengthCompressive strengthMaterials scienceEnvelope (radar)Geotechnical engineeringShear strength (soil)Composite materialStructural engineeringShear (geology)GeologyEngineering

Abstract

fetched live from OpenAlex

Mechanical characterization is important to the design and analysis of cemented paste backfill (CPB) structures. Unconfined compressive strength (UCS) tests have been widely used owing to their relative simplicity to characterize a material’s response to unconfined compressive loading. However, the UCS represents a single strength parameter and does not fully describe the material’s strength (or failure) envelope. In this study, we analyzed UCS tests with direct shear and uniaxial tensile strength tests conducted on the same CPB materials to provide mechanical characterization of CPB under a more complete range of loading conditions. The results demonstrate the Mohr–Coulomb failure envelope provides a consistent description of strengths arising from the three different test methods. Furthermore, a better estimate of the tensile strength is UCS/4, which is considerably higher than the conventional assumption that the tensile strength is equal to USC/10 or UCS/12. This has a significant impact on the assessed required strengths particularly for undercut designs using Mitchell’s sill mat analysis method and suggests that in future the conventional UCS tests should be complemented with direct tension and direct shear tests to improve underground designs using CPB.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.197
Teacher spread0.189 · 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 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

Citations9
Published2023
Admission routes2
Has abstractyes

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