Stochastic investigation of the relationship between track geometry and ballast degradation rates
Bibliographic record
Abstract
The railroad track system and its components are a critical transportation asset that is responsible for transmitting rolling stock wheel loads to the roadbed. To ensure safe and efficient operations, railroads perform frequent track inspections, some of which generate substantial amounts of track health data. Furthermore, with the rise of data science tools and methods, the potential for these data to move maintenance and safety towards more robust analysis is fostered. Recently, railroads around the world have increased their use of data trending for geometry prediction but they do not always cover how the boundary conditions are also changing over time. This study presents an evaluation of the relationship between the change of track geometry condition and ballast profile on both curved and tangent track segments. The stochastic approach proved to be a valid comparative method for the existing and emerging datasets. Track geometry profile degradation was shown to have significant correlation with both the Ballast Health Index (BHI) and initial value of profile. Additionally, profile degradation was found to be more accelerated in regions with poorer initial geometry profiles and higher initial BHI values. Findings have the potential to improve maintenance effectiveness and prioritization and provide a method for quantifying track degradation rates under different operating and maintenance conditions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".