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Record W4365504558 · doi:10.1139/cgj-2022-0379

Axial response of resin-encapsulated cable bolts in monotonic and cyclic loading

2023· article· en· W4365504558 on OpenAlexvenueno aff
Ashkan Rastegarmanesh, Ali Mirzaghorbanali, Kevin McDougall, Naj Aziz, Sina Anzanpour, Hadi Nourizadeh, Mahdi Moosavi

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGroutMaterials scienceComposite materialStructural engineeringStiffnessUltimate tensile strengthGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

The ease of use and the design flexibility of cable bolts have made them a popular choice for rock support. Cable bolts can be encapsulated with cementitious grout or resin. There is a need to better understand the impact of resins on the behaviour of cable bolts under varying load and stress regimes over their long service life. This study reports on 18 large-scale resin pull-out tests. The testing apparatus minimised the rotational movement of the cable at the exit point by using a fully grouted anchor tube. Six cable bolts, ranging from 50 to 100 tonnes in capacity, anchored using a fast-curing urea silica resin, were tested under monotonic and cyclic loading. Each cable type was tested twice in monotonic loading, and then the average initial peak load was used to generate a cyclic loading pattern. The study found that the resin product had a relatively low load capacity regardless of the cable type, and that loading type had a minimal impact on the results. The cable diameter had a minor influence on the pull-out results. The resin provided a stiff behaviour and an excellent response to repeated loading.

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)0.001
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 score0.167
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207