Quantifying the impact of rail longitudinal level changes on dynamic load magnitudes through instrumented wheelset measurements
Bibliographic record
Abstract
Track geometry significantly influences train-track dynamic interactions, primarily due to irregularities at the wheel-rail interface. These irregularities generate dynamic load excitations, leading to defects and damage. This study evaluates how track surface profile longitudinal level vertical changes affect vertical wheel-to-rail loads, quantified by the dynamic load factor (ϕ), due to the importance of ϕ in track design. A limitation of the existing studies is their focus on instrumented sections and numerical modellings without considering different track structures. This study conducted dynamic load measurements using two instrumented wheelsets (IWS) over a 340 km section operated by a North American Class 1 freight railway in the Canadian Prairies under various track structures and train speeds. Rail surface longitudinal level measurements were obtained as routine measurements through a track geometry car. The paper presents statistical distributions of rail longitudinal level across different track structures and evaluates two widely used and recently developed ϕ equations within the context of North American freight railways to quantify the impact of rail longitudinal level (i.e. profile) changes on dynamic load magnitudes. The study also evaluates regulated threshold values by considering observed trends in the measured data, which aims to quantify and compare these values with our current understanding.
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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".