Design and Installation of a Geotechnical Monitoring System for Monitoring Freeze-Thaw Cycles on a Railway Track
Bibliographic record
Abstract
Climate change might increase the frequency of events such as heat waves, freeze-thaw cycles (FTC), and flooding, and more specifically in permafrost rich regions. These climate hazards are expected to have an impact on railway track performance. There is little publicly available data on their quantitative impacts on railway operations. Such quantitative data is essential for determining when, where, and to what extent climate adaptation measures are needed. Freeze and thaw cycle results in frost heave and thaw softening in track foundation (substructure). Both frost heave and thaw softening may lead to unsafe operating conditions especially for rail transit and passenger rail systems as their high operating speed makes them much less tolerant to deviations in track geometry parameters. In order to investigate the effects of a freeze-thaw cycles on an active railway, a structural and geotechnical monitoring system was designed and installed on a section of VIA’s track in Ontario. The instruments measure various track parameters such as pore water pressure, heave, and deformation at different depth within track foundation, track temperature, strain in the rail, and track surface deformation during freeze-thaw cycles. The data logging system relays static data and high speed data that are triggered by train passages. We show that the selection of instruments and design of the data logging system provide relevant geotechnical data in a manner that could be applied to northern regions and introduce recommendations for future installations. Moreover, we discuss the installation methods appropriate for cold climates because some instruments are temperature-sensitive. Since such systems typically need to be self-sufficient special considerations have to be taken to account for the relatively high power requirements of dynamic monitoring. The suggested system is shown to be useful for track monitoring projects in permafrost-rich regions where freeze-thaw cycles are a concern.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".