Assessing the Potential of a Technology to Map the Subgrade Stiffness Under the Rail Tracks
Bibliographic record
Abstract
Canadian railways pass through a wide variety of terrains, with the most problematic foundation soils being the glacio-lacustrine clays and very soft muskeg. Soft subgrade materials are prone to sudden failure and large plastic deformation which are a safety concern for rail operations. The locations of soft subgrade are not known due to the lack of an economical method to map the extent of track stiffness. A new technology developed at the University of Nebraska–Lincoln (UNL) (Greisen, 2010; McVey et al., 2005) allows for the measurement of the vertical deflection of the track structure under constant axle loads moving at normal track speeds. This paper presents the preliminary findings from testing of the technology to map the subgrade stiffness on Canadian National’s Lac la Biche subdivision (LLBS). The resultant data showed that the system is sensitive to the effects of track joints. This sensitivity obscures the larger scale variations in deflection due to soft subgrades. To mitigate the impact of the joints, a simple filtering procedure has been proposed to interpret the data at a large scale. The filtered data are used to map the relative stiffness of the subgrade along the LLBS. In addition, the historical track geometry defects data on the LLBS has been used to quantify the impact of soft foundations on degrading track geometry. The preliminary results show that the UNL system has the potential to be used as a measure of rail track performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".