Enhancing the stability of railroad ballast with geogrid reinforcement: an experimental and discrete element modeling study
Bibliographic record
Abstract
Canada possesses an extensive rail network that is mainly supported by ballasted substructures in which a ballast layer lies immediately beneath the rail-tie assembly.The ballast layer performs multiple key functions in a track structure that include supporting the tracks, maintaining their alignment, and transferring train loads to the underlying soil layers.Due to its unbound nature, ballast undergoes substantial deformations when exposed to train loading that disturb the track alignment and compromise the track riding safety.Geogrids have recently emerged as a viable means to stabilize ballast and mitigate its deformations.A geogrid's ability to reinforce ballast hinges on its interaction with ballast particles, which is a function of parameters such as the geogrid aperture size and location in the ballast layer as well as the subgrade strength that must be investigated.Additionally, geogrids tend to exhibit temperature-dependent mechanical properties.Considering that Canadian railroads tend to be exposed to significant seasonal temperature fluctuations, it is important to determine whether such changes impact the performance of geogridreinforced ballast.invaluable advice, guidance, and unwavering support throughout my Ph.D. I also
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".