The application of distributed optical strain sensing to measure the strain distribution of ground support members
Bibliographic record
Abstract
A distributed optical strain-sensing technique is presented as a solution for measuring the strain distribution along ground support members used in tunnelling and mining works. The technique employs a Rayleigh optical frequency domain reflectometry technology, which measures strain at a spatial resolution of 0.65 mm along the length of a standard optical fiber. A rationale for selecting this technology as a potential monitoring technique for ground support elements over alternative commercially available technologies is discussed. The development of a technique to couple optical fiber sensors with rock bolt, umbrella arch, and cable bolt support members is also demonstrated. A robust laboratory investigation of such optically instrumented support members demonstrated the capability of the technique to capture the expected in situ support behaviour in the form of coaxial, lateral, and shear loading arrangements as would be anticipated in the field. Moreover, the micro-scale data obtained by this optical sensing technique are shown to provide unprecedented insight into the local/micro-scale geomechanistic complexities associated with the bearing capacity of ground support members, especially when compared with data obtained by discrete strain-sensing technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".