Two Decades’ Worth of Lessons Learned from the Use of Distributed Fiber Optics for Ground Characterization and with Tunneling
Bibliographic record
Abstract
Over the past two decades, the author along with his research group and industrial partner began to develop a novel Rayleigh-based distributed optical strain sensing technology capable of monitoring strain along a ground support scheme at sub-centimeter spatial resolution. This came out of the requirement to monitor and understand the geomechanics of composite ground support systems at the micro-scale. This line of research has been extended to monitor fully grouted rock bolts (FGRB) and rock mass deformation with an increased resolution of sub-millimeter spacing (0.65 mm). This distributed optical sensing technique has been tested in the laboratory and been implemented at multiple sites around the world. Within this context, this paper summarizes key lessons learned (i.e., spatial resolution, QA & QC, and industrial applications) over the past two decades regarding the use of fiber optics for monitoring the ground conditions and support elements in underground excavations. Through such monitoring, the goal is to improve current design and guidelines.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".