Fibre optic sensing for thermal stress measurement
Bibliographic record
Abstract
Continuously welded rail (CWR) is one of the most common railway track constructions. However, large thermal stresses can build up in CWR due to the elimination of expansion gaps leading to rail breaks in cold temperatures and rail buckling in hot temperatures. When rail temperatures are greater than the rail neutral temperature (RNT), compressive stress is induced, while temperatures below RNT induce tensile stress. Technical reviews completed by National Research Council Canada identified a gap in knowledge in quantifying the level of stress being experienced by rails in CWR. Fibre optic sensing was identified as a promising method to measure and monitor long lengths of track in order to better understand thermal stresses in-situ. This research is focused on evaluating the suitability of a select number of fibre optic sensing systems for thermal stress measurement by evaluating these systems, both analysers and optical sensing fibres, within a laboratory setting. This paper provides a brief background on the use of fibre optic sensing before presenting the experimental campaign, including tests conducted on rail segments instrumented with a variety of fibre optic sensors under load as well as when exposed to temperature changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".