Enquête sur le givrage des selles de rail / le soulèvement par la glace («ice jacking») : Phase II
Bibliographic record
Abstract
Tie plate icing, also known as ice jacking, is a cold-weather railway phenomenon involving the accumulation of ice and/or snow between the tie plate and the base of the rail. This issue poses significant safety risks in cold climates and has led to derailments in the past. Phase II of this research aimed to further investigate the root causes of tie plate icing through a comprehensive approach, including a railroad industry survey, a track panel test, and finite element (FE) modeling. The study was conducted between December 2022 and June 2023. A railroad survey was distributed among members of the Railway Research Advisory Board (RRAB), Class I railroads, regional railroads, and short lines, receiving 11 responses. Among them, nine respondents confirmed experiencing tie plate icing in their territories. The survey documented track issues caused by tie plate icing, areas of concern, identification methods, and remediation strategies. The findings indicated that tie plate icing can occur anywhere along the track, with visual inspection and manual removal being the primary methods for identification and mitigation. To further explore the effects of tie plate icing, a 32-tie track panel was built to simulate the phenomenon under controlled conditions. Steel shims of varying thicknesses (¼″ and ½″) were used to replicate different severities of tie plate icing. The track panel test results showed that track gage strength was strongly correlated with the number of ties affected by tie plate icing and the vertical displacement of the rail above the tie plate. The largest observed gage widening was 0.38″ when five consecutive ties simulated tie plate icing and missing field-side rail spikes. Furthermore, an increase in shim thickness from ¼″ to ½″ resulted in an additional 0.19″ gage widening. The track panel test results were used to validate an FE model, which was then employed to simulate more severe conditions. The model confirmed that while tie plate icing alone did not exceed 1″ of gage widening under a 4-kip load, additional factors, such as missing spikes and degraded ties, significantly increased gage widening beyond safe limits. The study concludes with recommendations for tie plate icing mitigation, including enhanced track inspections, addressing high and missing spikes before winter, and improving ballast drainage. The findings provide valuable insights for railroads to proactively manage tie plate icing and enhance track safety in cold-weather environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".