Use of fiber optic sensing to measure distributed rail strains and determine rail seat forces under a moving train
Bibliographic record
Abstract
Rayleigh backscatter fiber optic sensing permits dynamic strains to be measured along an optical fiber with a gauge spacing and temporal resolution sufficient for rail applications. However, this sensing technology is highly sensitive to vibration. A 7.5 m long section of rail was instrumented with optical fiber and strain measurements were recorded during passage of a freight train slowed to 8–11 km/h. This strategy to minimize rail vibration was successful in permitting distributed dynamic rail strains to be measured under freight car loading. The measured rail strains were used to determine the rail shear forces, which were then used with the static wheel loads to determine the rail seat load for 14 consecutive sleepers as the train passed over the field monitoring site. These data were then combined with measurements of dynamic rail displacement captured using digital image correlation to infer the rail seat load–deflection relationships for individual sleepers. These relationships were observed to provide significantly more detailed information about unsupported voids and the sleeper contact stiffnesses than the traditional consideration of the relationship between applied load and rail deflection and highlights how track behavior at a monitored location can be dependent on the conditions and behavior of neighbouring sleeper.
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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 | 0.000 |
| 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 | 0.000 |
| 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".