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A laboratory-based approach to assess rockbolt behaviour in shear

2017· article· en· W2771139528 on OpenAlexafffundabout
G.J. Snell, Evan Kuley, Douglas Milne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaMosaic Company
KeywordsRebarEpoxyMaterials scienceShear (geology)Composite materialStructural engineeringPotashShearing (physics)Strain gaugeTension (geology)Geotechnical engineeringGeologyMetallurgyEngineeringUltimate tensile strength

Abstract

fetched live from OpenAlex

Epoxy-bonded rebar is commonly used to support the roof of an underground potash mine. The rebar is often loaded in tension, but in certain circumstances, the potash can load the rebar in shear or by a combination of tension and shearing. Rebar failure due to tension has been studied previously and is well understood. A laboratory test procedure has been developed to help quantify rebar shear behaviour. The rebar is tested by bonding it in three segments of steel pipe and applying a lateral load to the centre segment. These tests isolate the rebar–epoxy interaction from the highly variable effect of the surrounding potash. The test method also allows for an easy and controlled comparison of different sizes of rebar, grades of rebar steel, and aperture between the pipe sections; all of which can significantly affect the behaviour of rebar in shear. The data gathered during each test includes the load and displacement. However, distributed optical strain sensing is also being used, which allows for strain measurements at 0.5 mm resolution along the length of the rebar. This technology allows for detailed insight into rebar behaviour. The International Minerals Innovation Institute (IMII), PotashCorp, The Mosaic Company, Agrium Inc., as well as Natural Sciences and Engineering Research Council of Canada (NSERC) are sponsoring this research project.

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 score0.492
Threshold uncertainty score0.524

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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

Citations6
Published2017
Admission routes3
Has abstractyes

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