Longitudinal strain monitoring of rails using distributed and discrete sensors
Bibliographic record
Abstract
With one of the most extensive rail networks in the world (48,000 km of track), rail transportation is vital to Canada. Newer, more powerful locomotives hauling heavier freight at higher operating speeds are capable of producing twice the tractive effort of older locomotives. This has raised concerns regarding the risks associated with exceeding the longitudinal load carrying capacity of in-service rail support systems (e.g. bridges). There is now a growing need to investigate the longitudinal stresses that rails are being subjected to and to evaluate the potential requirement for rail rehabilitation. Past studies exploring structural health monitoring (SHM) techniques for rail infrastructure have focused on the application of discrete strain sensor technologies making it difficult to understand the rail’s full strain profile under locomotive loading. This paper introduces a series of dynamic field tests conducted to assess the suitability of both distributed and discrete sensors to measure longitudinal rail strain under locomotive braking. A distributed fibre optic sensor based on Rayleigh backscatter in addition to adhesive and friction bonded foil strain gauges were used. Conclusions will be drawn as to the most applicable rail monitoring technology and future research directions will be outlined.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".