MétaCan
Menu
Back to cohort
Record W4404083327 · doi:10.17159/2411-9717/3294/2024

Behaviour of paddled energy-absorbing rockbolts under complex loading laboratory conditions

2024· article· en· W4404083327 on OpenAlexaff
Greig Knox, John Hadjigeorgiou

Bibliographic record

VenueJournal of the Southern African Institute of Mining and Metallurgy · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Since their introduction in 2010, paddled energy-absorbing rockbolts have been widely used in seismically active hard rock mines. This paper provides new data and reviews previous work to quantify the performance of paddled energy-absorbing rockbolts under controlled laboratory conditions. Of significance is the realization that the typical split location in impact tests, at the centre between two paddle anchor points can at best provide an upper limit value. This inherent variability in performance under different testing configurations should be acknowledged and taken into consideration in the design of ground support in seismic conditions. This paper discusses the reduction in capacity of paddled rockbolts as a function of loading angle from a maximum value during axial tests (0° loading angle) to the lowest value during pure shear (loading angle of 90°). A significant reduction in displacement capacity is observed as the loading angle changes between axial (0°) and 40°. Beyond a 40° loading angle up to shear (90°) loading the reduction in displacement capacity is less significant. Due to the variances in the capacity observed through the variance of the testing configuration: loading mechanism, direction of loading, location / presence of a discontinuity, it must be recognized that results from laboratory-based testing are not independent of the testing configuration. This should be acknowledged when extrapolating anticipated performance in the field.

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 score0.266
Threshold uncertainty score0.408

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Southern African Institute of Mining and MetallurgySame topicGeotechnical and Geomechanical EngineeringFrench-language works237,207