Evaluating railway track support stiffness from trackside measurements in the absence of wheel load data
Bibliographic record
Abstract
It is generally accepted that track support stiffness is a major factor controlling rates of track geometry deterioration, particularly where the track support stiffness changes abruptly. There is, therefore, considerable potential benefit in being able to quantify and detect changes in the track support stiffness. In recent years, trackside techniques using various types of transducers have been developed to determine track deflections as trains pass. However, deducing the track support stiffness from these measurements requires assumptions to be made concerning train loading and track behaviour, and the possibility of different interpretations remains. For example, loads from moving trains vary dynamically and it is not usually feasible to measure their exact values at any given point along the track. This paper presents new methods of analysis, which can be applied to frequency spectra of track displacement, velocity or acceleration generated as trains pass to calculate the track support stiffness for trains of known axle intervals, without needing to know the actual loads applied. The approach is demonstrated with reference to theory and measured data from a range of field sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".