Algorithm to Estimate the Lateral Position of Wheel-Rail Contact and Corresponding Rail Profile Radius
Bibliographic record
Abstract
This article develops and validates an algorithm to estimate the lateral position of wheel-rail contact and the corresponding rail profile radius. The lateral contact position and contact radius are two novel rail profile performance measures that enable more refined characterization of the rail profile and its influence on rail wear and vehicle dynamics. Leveraging recent advancements in rail profile monitoring techniques, the algorithm contributes to rail maintenance research and practice by developing new measures of performance based solely on commonly available rail profile data. The algorithm developed in this article is an automated process that estimates the lateral position of wheel-rail contact and the corresponding rail profile radius along a rail segment. It uses measured rail profile data as an input and applies rigid contact theory to model contact between a linear wheel profile and the rail profile. The lateral contact position and contact radius are calculated using computer programming that provides graphical and numerical results on a profile-by-profile basis as well as summary statistics for each rail segment. This methodology produces expected results when subjected to validation tests. The validation process analyzes the rationality of algorithmic output against a series of expected results using rail profile data from selected tangent segments of a closed-loop captive-fleet North American rail transit property. The algorithm output does not significantly deviate from any of the expected results, and as such, the algorithm is considered validated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".