Bridge transition monitoring: Interpretation of track defects using digital image correlation and distributed fiber optic strain sensing
Bibliographic record
Abstract
Railway bridge transitions represent locations of vertical stiffness variations that are believed to amplify the dynamic wheel loads which contribute to the development of both differential track settlement and hanging sleeper issues that are difficult to resolve despite the current knowledge of bridge transition behaviour. This paper presents a field monitoring study of a railway bridge transition in which track defects – including both gaps between the rail and sleeper plates, i.e. “rail–sleeper gaps,” and rail flange scrape marks exposing bare steel – were observed during a visual inspection of the transition. Trains were monitored by measuring both track displacements using Digital Image Correlation and distributed rail strains using a Rayleigh-based fiber optic analyzer. Through analysis and interpretation of the collected monitoring data, it was found that measurements of rail–sleeper gaps could be used to obtain a first-order estimate of the shape of the differential track settlement profile. Additionally, it was found that measurements of scrape marks on the rail flange could be used to estimate the extent of longitudinal rail movement that could occur during train passage, and that the loads applied to the bridge structure were influenced by the nature of the rail–sleeper gaps at the monitoring site.
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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 | 0.001 |
| 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) | 0.001 | 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".