Innovative Approach to Establish Transitions Between Two Materials of Different Frost Susceptibilities Under Rigid Concrete Pavement at Montreal-Pierre Elliott Trudeau International Airport
Bibliographic record
Abstract
Natural soils at the site of Montreal-Pierre Elliot Trudeau International Airport are not easily reusable as trench backfill for laying underground infrastructure (sewers, fuel pipeline) since they generally have a high proportion of fine particles (< 80 micrometres) along with a high water content, making them difficult to compact. Previously, it was common practice to fill trenches with a stone screening containing more than 15 percent fine particles (< 80 micrometres) so as to mimic as much as possible the frost behaviour of the existing natural soil. However, this process raised another issue, as during the rainy season, these materials (stone screening) became easily saturated and thus unstable, leading to subgrade's instability. Given this recurring problem, especially in the fall, ADM decided in 2012 to fill these infrastructure trenches with crushed stone close to a 0-20 mm calibre or 0-56 mm calibre so as to ensure the stability of the materials laid. As a result, the frost susceptibility of the crushed stones in these trenches was completely different from that of the surrounding natural soils. This lecture will present the innovative approach put forward by the authors to design an appropriate transition between these two materials, for a rigid concrete pavement, using the SSR model for frost heave calculation used by Quebec's Department of Transportation (MTQ) in its CHAUSSEE 2 software. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".