Demonstration of the detection of buckling effects in steel pipelines and beams by the Brillouin sensor
Bibliographic record
Abstract
Pipeline failures induce costly repair and cleaning spending that could be avoided by the implementation of proactive approaches. Distributed sensors based on Brillouin scattering are attractive candidates to monitor structural health of pipelines. They can measure local strain and allow real-time control over lengths ranging from a few meters to tenths of kilometres. One of the possible degradations that must be detected is buckling. We describe in this paper what is to our knowledge the first time report of buckling detection with a Brillouin sensor. We conducted an experiment reproducing buckling monitoring in a laboratory environment. Two specimens (steel pipeline and beam) were prepared by locally thinning the inner wall to provoke buckling. Fibre was laid along the external walls of the specimens. Strain gauges were glued in thinned wall area. An axial load was applied to the specimens and increased while strain measurements were carried out with the Brillouin sensor and the strain gauges. All the measurements showed a progressive compression increase in the neighbourhood of the thinned wall. Finally buckling aroused and was visually identified as well as localized with the Brillouin sensor. Strain gauges readings and strain measurements with the Brillouin sensor were in good agreement.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".