Pre- and Post-Improvement Performance of a Railway Embankment Stabilized with Ductile Inclusions
Why this work is in the frame
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Bibliographic record
Abstract
Large deflections and accumulation of permanent track geometry defects at railway soft spots cause significant operational issues including increased maintenance costs, decreased train speed, and, at an extreme, derailments and track failures. Reinforcing inclusions placed between the ties are a promising rehabilitation scheme to locally stabilize the embankment and transfer the loads to deeper, more suitable soils. In this paper we report the results of a long-term monitoring program to assess the performance of ductile inclusions, known commercially as “GeoSpike® elements”, to stabilize a rapidly deteriorating railway soft spot. Observations of track bed behavior are presented for 8 months prior to installation and 15 months after installation using a combination of LIDAR, Digital Image Correlation (DIC), and ShapeAccelArrays (SAA). Through the monitoring program, it was determined that the ductile inclusions decreased the average rate of deterioration by a factor of approximately 3, which allowed the frequency of site maintenance to be decreased from three times a year to once a year.
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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 | 0.000 | 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) | 0.000 | 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 it