Distributed Sensing of Circumferential Strain Using Fiber Optics during Full-Scale Buried Pipe Experiments
Bibliographic record
Abstract
As buried infrastructure in North America and around the world reaches the end of its service life, engineers and infrastructure managers will require an improved understanding of the performance of both deteriorated pipes and repair techniques. To develop this improved understanding, sensing technologies that enable the full pipe behavior to be measured, rather than a small number of localized discrete measurements, are required. A possible solution to this problem is to use distributed fiber optic strain sensors. To this end, a series of buried pipe tests were undertaken on steel, concrete, and high-density polyethylene (HDPE) pipes instrumented with distributed fiber optic strain sensors. The distributed measurements were in agreement with conventional strain gauges, but enabled the full strain distribution around the circumference of the pipe to be measured. This allowed localized behavior that would have been missed with conventional strain gauges to be detected and quantified. In addition, the choice of fiber optic cable proved to be an important consideration due to a trade-off between measurement accuracy and sensor robustness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".