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Record W4399555567 · doi:10.1016/j.jrmge.2024.04.008

Large-scale laboratory investigation of pillar-support interaction

2024· article· en· W4399555567 on OpenAlexaff
Akash Chaurasia, Gabriel Walton, Sankhaneel Sinha, Timothy Batchler, Kieran Moore, Nicholas Vlachopoulos, Bradley Forbes

Bibliographic record

VenueJournal of Rock Mechanics and Geotechnical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsBGC Engineering (Canada)Royal Military College of Canada
FundersAlpha Foundation for the Improvement of Mine Safety and Health
KeywordsPillarScale (ratio)Computer scienceEngineeringGeographyMechanical engineeringCartography

Abstract

fetched live from OpenAlex

Underground mine pillars provide natural stability to the mine area, allowing safe operations for workers and machinery. Extensive prior research has been conducted to understand pillar failure mechanics and design safe pillar layouts. However, limited studies (mostly based on empirical field observation and small-scale laboratory tests) have considered pillar-support interactions under monotonic loading conditions for the design of pillar-support systems. This study used a series of large-scale laboratory compression tests on porous limestone blocks to analyze rock and support behavior at a sufficiently large scale (specimens with edge length of 0.5 m) for incorporation of actual support elements, with consideration of different w/h ratios. Both unsupported and supported (grouted rebar rockbolt and wire mesh) tests were conducted, and the surface deformations of the specimens were monitored using three-dimensional (3D) digital image correlation (DIC). Rockbolts instrumented with distributed fiber optic strain sensors were used to study rockbolt strain distribution, load mobilization, and localized deformation at different w/h ratios. Both axial and bending strains were observed in the rockbolts, which became more prominent in the post-peak region of the stress-strain curve.

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: none
Teacher disagreement score0.937
Threshold uncertainty score0.650

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 integrity0.0000.001
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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
Published2024
Admission routes1
Has abstractyes

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