Track displacement and energy loss in a railway embankment
Bibliographic record
Abstract
The mainline railway track between Dublin and Belfast was constructed during the 1850s, with substantial lengths of railway embankment constructed over soft, peaty soils. In recent years Northern Ireland Railways (NIR) has noticed that the sections of the railway track constructed on these peaty soils have been deteriorating at an increasing rate. Train speeds have been reduced in response to concerns that cyclic track displacements appear to be increasing over time in response to train loading. Track maintenance has also increased significantly. The research described in this paper was undertaken to quantify the response to cyclic train loading of two portions of this track founded on peaty soils. Track displacements were recorded using a sensor system specifically created for this project. The sensor consisted of a photosensitive array, mounted on the sleepers, and a laser, which was targeted onto the photosensitive array from a location outside the area of influence of train loading. Track deflections from 5 to 20 mm were measured under train speeds from near zero to over 120 km/h. The temporal variation in track displacement was used to calibrate an analytical (Winkler) model. This analysis suggests that the deformation of the embankment under train loading was not due to dynamic excitation but rather to static deformation of the poor-quality fill and soft foundation materials. As a consequence, the analytical model highlighted that train speed has limited effect on the magnitude of the deflection of the embankment within NIR operating speeds, but has the potential to significantly reduce the power lost to the damping within the embankment and subgrade.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".