Moisture and soil strength monitoring of a railway embankment remediated with wicking geotextile
Bibliographic record
Abstract
A 45 m section of a railway embankment located at Fort Saskatchewan County in Alberta, Canada, was remediated as a part of the Canadian Pacific Railway’s (CP) Grade Stabilization/Remediation Plan. The embankment materials were replaced while a 4.6 m wide reinforcing geotextile (Mirafi® RS580i) and a 7.3 m wide wicking geotextile (Mirafi® H2Ri) were installed in the ballast and sub-ballast interface and between the subgrade and sub-ballast, respectively, aiming to address issues such as poor drainage and moisture retention. The studied site consists of an instrumented track including a remediated and an adjacent control section that provided the opportunity to measure volumetric water content (VWC) within the sub-ballast and clayey subgrade at both configurations. The VWC variation with seasonal weather change is continuously monitored by nine moisture sensors, and an antecedent precipitation index (API) model was developed to evaluate the influence of precipitation events on the VWC in both sections and to interpret the impact of the in situ VWC on the unsaturated strength of the soil according to the soil-water characteristic curve (SWCC) results. An initial evaluation of the moisture-suction relationship has shown that the subgrade soil strength is improving within the remediated section; nonetheless, these trends are anticipated to be more consistent with long-term observation.
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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) | 0.000 | 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 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".