Railway Dynamic Load Factors Developed from Instrumented Wheelset Measurements
Bibliographic record
Abstract
Dynamic load factors (ϕ) relate the magnitude of vertical wheel-to-rail loads in operation (dynamic loads) to static loads resulting from the weight of the rail car and its contents, as a function of train speed. The ϕ equations are often used in the selection of rail steel and cross-sections (weight). Equations for ϕ have been put forth by the American Railway Engineering and Maintenance-of-Way Association (AREMA) and others. A limitation of the existing ϕ equations is that they have been derived from loads measured at instrumented sections of track and observe the many wheel loads but with constant track conditions. For this study, measurements of dynamic loads from two instrumented wheelset (IWS) as it conducted four passes over a 340 km section of track operated by a North American Class 1 freight railway through the Canadian Prairies. These measurements provided dynamic loads from one loaded freight car over various track structures at differing train speeds. This paper presents the IWS data sets and the variation of dynamic loads between multiple passes of the section of track studied, and the statistical distribution of dynamic loads. New ϕ equations are developed for tangent track and nontangent track (inclusive of bridges, grade crossings, curves, and switches).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".