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Record W4376129500 · doi:10.1139/cgj-2022-0619

Investigating shear behaviour of fibreglass rock bolts reinforcing infilled discontinuities for various pretension loads

2023· article· en· W4376129500 on OpenAlexvenueno aff
Peter Gregor, Ali Mirzaghorbanali, Kevin McDougall, Naj Aziz, Behshad Jodeiri Shokri

Bibliographic record

VenueCanadian Geotechnical Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringShear (geology)Classification of discontinuitiesDirect shear testFailure mode and effects analysisStructural engineeringGeologyMaterials scienceEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

In this paper, eight shear tests were carried out utilising a double shear with infilled shear interfaces after determining an appropriate experimental design and modified testing scheme. For this, two rock bolts, 20 and 30 tonnes, were tested with modified double shear testing apparatus at different pretension loads. The infilled test scheme was conducted with 5 mm thick sandy clay infilled shear interfaces. A 40 MPa small aggregate concrete was used for all samples to simulate the host rock. Based on the shear profiles, it was found that all samples followed a three-part failure profile consisting of elastic, strain softening, and failure regions. The results of double shear tests revealed that the 20 tonne rock bolt saw a significant overall decrease of approximately 30% in its failure displacement response as opposed to the 30 tonne samples, which saw a 6% increase. Also, it was concluded that the 30 tonne samples outperformed the 20 tonne rock bolts by up to 30%, irrespective of initial pretension settings. Comparing the physical failure characteristics of the samples found that both the 20 and 30 tonne samples exhibited the same response to increased pretension. As the pretension increased, the angle experienced at the hinge point also increased incrementally.

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)0.001
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 score0.117
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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