Investigating the impact of frost heave and thaw softening on changing the vertical force at wheel/rail interface
Bibliographic record
Abstract
The frost heave results in non-uniform deformation and irregularity on railway track. These irregularities increase the dynamic response of the train-track system, resulting in rapid deterioration of track geometry, and poor ride quality. Large surface roughness may cause unloading of wheels and consequently could lead to derailment. This paper presents the results of employing NUCARS® (New and Untried Car Analytic Regime Simulation) software to evaluate the interaction between rail car and the track as it passes through the frost bumps measured over a railway track section during two freeze-thaw monitoring seasons. The vertical wheel/rail forces (force exerted on the wheel by the rail) at each wheel resulting from the simulation is compared against Association of American Railroads (AAR) standard (Chapter 11 of the AAR Manual of Standards and Recommended Practices Section C - Part II) to determine how passing through the frost susceptible sections may affect the safety of train operations. According to AAR Specification M1001 Chapter XI track worthiness limits, the minimum vertical wheel load should be greater than 10% of the static condition. Using the measured track deformation at a study site located on VIA Rail subdivision in eastern Ontario, the minimum vertical force of 72% of the static load was observed as a result of using the track profile measured during thawing season. This value, which is well above the AAR’s 10% requirement, occurred at the culvert location where there was large non-uniform deformation. Also, by comparing the results of minimum and maximum vertical force for track in various stages of freezing-thawing cycle, it was observed that the thawing stage is creating the worst combination of the forces.
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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 | 0.000 | 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) | 0.002 | 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".