Quantifying the Impact of Subgrade Stiffness on Track Quality and the Development of Geometry Defects
Bibliographic record
Abstract
This paper presents the quantification of the impact of subgrade stiffness on the prevalence of track geometry defects and degradation of track quality indices (TQIs). The data included in this study come from two high-traffic subdivisions [>50 million gross tonnes (MGT)/year] in Canada with a total length of 800 km and consist of vertical track deflection (VTD) measurements and 3 years of track geometry measurements. The VTD measurements were used to derive two indices that represent the magnitude and variability of the subgrade stiffness. An analysis of the data shows that the locations at which defects occur correspond to locations with low modulus (higher VTD) and high variability of track modulus. A similar correlation is shown with track roughness represented by a TQI. However, the correlation with the spectrum of TQI calculated was found to be poor. This was attributed to maintenance activities carried out to improve track conditions. The correlation with the TQI greatly improved when arbitrary thresholds were applied and TQI values above this were treated as geometry defects. These results show that the locations that have a low modulus and high variability in the modulus are those that are the most difficult to maintain and at which maintenance is not always able to keep up with the degradation of track geometry. Thus, VTD measurements evaluate the underlying causes that result in the degradation of track conditions and allow for the identification of sections where poor track conditions are most likely to develop.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".