A rail corrugation index to characterize noise impacts and grinding effectiveness on rail transit systems
Bibliographic record
Abstract
Despite general agreement that rail corrugation generates unwanted noise, there is a need to quantify the relationship between rail corrugation and noise and to leverage this relationship within preventive rail grinding programs. This paper develops a novel rail corrugation index (RCI) and demonstrates the suitability of that index to characterize the relationship between corrugation and noise, to assess grind effectiveness, and to predict noise as a function of rail corrugation. Using a time-series data set collected at a North American rail transit property, the proposed RCI illustrated corrugation growth as a function of accumulated tonnage and an expected reduction of corrugation after grinding. The RCI also correlated well with corresponding wayside noise observations. The evident response behavior and the relationship between rail corrugation and wayside noise gave rise to the assessment of grind effectiveness using the RCI. Further, a modelling effort demonstrated that noise can be predicted using the RCI calculated from only one rail (left or right) on a tangent section. This result suggests that there may also be an opportunity to predict corrugation using noise data, thus limiting the need for track downtime required to measure corrugation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".