Effects of Rib Spacing and Grout Annulus on Grouted Rock Bolt Performance Utilizing Fiber Optic Strain Sensing
Bibliographic record
Abstract
An often-utilized solution in terms of providing support to underground excavation, the fully grouted rebar rock bolt system presents optimization potential due to existing technological limitations in capturing and understanding its composite system response. In order to address these limitations, the development and application of Distributed Optic Fiber Sensing (DOS) allows for continuous strain monitoring (at a spatial resolution of 0.65 mm utilizing the technique defined herein) across a full spectrum of loading. A robust laboratory investigation was conducted featuring 24 rock bolt specimens. This examined the effects of two selected independent variables: rib spacing (from 13 mm to 68 mm) and grout annulus (from 7.7 mm to 22.8 mm). This body of research provides valuable insight into the performance of grouted rebar rock bolts and the effects of the selected parameters (rib spacing and size of the grout annulus), while also highlighting an advanced monitoring technique. Results indicated that rib spacing was a negative predictor of bond performance. No definite conclusions were drawn in terms of the effect of the size of grout annulus; however, findings provide limited support for an optimal sizing in relation to rib height. The results were also compared to analytic and numerical models. These insights can aid in calibrating and validating numerical models, and improve monitoring and rock bolt design within the overall goal of improving and optimizing ground support design arrangements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".