Measurement of vertical and longitudinal rail displacements using digital image correlation
Bibliographic record
Abstract
Excessive rail displacements can result in reduced rail traffic speeds and increased risk of derailments. A number of methods exist for the measurement of vertical rail displacements, including using geophones, high-speed cameras, and rail vehicle mounted systems. The advantage of rail vehicle mounted methods is that large lengths of track can be assessed. However, there are some instances where the measurement of absolute rail deformations is essential, particularly in poor subgrade conditions where significant long-term settlements are possible. Vehicle-mounted monitoring strategies cannot capture the so-called “running rail” phenomena, where the passage of a train can push the rails longitudinally. Monitoring rail displacements using digital image correlation (DIC) has the potential to capture both of these phenomena unlike other technologies. The objectives of this paper are to evaluate the use of a system of synchronized high-speed cameras to measure absolute longitudinal and vertical rail displacements using DIC, to observe what factors influence the relative magnitudes of these displacements, and to investigate whether DIC measurements can be used to evaluate the stiffness and damping parameters required to develop the displacement–time response of a rail foundation system. The DIC system was evaluated at two sites: one with a high-quality subgrade and one with a peat subgrade. The DIC system was able to capture the absolute vertical and longitudinal displacements due to the passage of trains at both sites. The data from one of the sites, with the high-quality subgrade, were used to develop parameters for system stiffness and damping.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".