Evaluating the impact of ballast undercutting on the roughness of track geometry over different subgrade conditions
Bibliographic record
Abstract
The progressive degradation of railway ballast is often cited as a primary factor that contributes to the development of track roughness, while ballast renewal (undercutting) attempts to manage its long-term development. Soft subgrades have been shown to strongly influence track geometry and are a contributing factor that has not been considered during conventional track maintenance. This study evaluated the impact of undercutting on long-term trends in track geometry roughness, and what impact softer subgrades had on the effectiveness of undercutting. A combined 6.90 km of Class II–IV heavy-haul track in Western Canada (undercut in 2010 and 2011) formed the basis for this analysis. Annual traffic on these sections typically totals 50 million gross tonnes. Long-term trends in the track crosslevel, alignment, and surface roughness after ballast renewal were derived from 50 track geometry surveys carried out over a five-year period (2010–2015). The results showed that undercutting significantly reduced track roughness over sand, silt, clay, or till subgrades; however, it was often ineffective when used over soft organic subgrades. Thus, while ballast degradation is the primary cause of track roughness in segments constructed on mineral subgrades, it is not a mechanism that results in track geometry roughness over soft organic soils.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".