Thermal Behavior of Flexible Pavement Containing Foam Glass Aggregates as Thermal Insulation Layer
Bibliographic record
Abstract
In cold regions, differential frost heave during winter and bearing capacity loss during spring, induced by seasonal temperature variations, lead to several types of surface profile deterioration. Thermal insulation is commonly used as a preventive measure to limit the frost penetration in frost sensitive subgrade soil, thus reducing the associated damages and rehabilitation costs. In Canada, extruded polystyrene is widely used for pavement insulation. However, new alternative materials are now available, including foam glass aggregates made from recycled glass of various origins. Foam glass aggregates can be considered as a lightweight and insulating granular material. This research focuses on the thermal behavior of this material in two case studies, i.e. in the laboratory and on site. The laboratory-controlled pavement section was built in a pit where thermal conditions and water table levels are precisely controlled. Using in situ road sections built on frost sensitive soils, several flexible pavement design techniques used in Québec were compared. The first section was insulated using foam glass aggregates designed following European producer recommendations, the second was insulated using extruded polystyrene panels, and the third is a conventional pavement structure without insulation. The thermal and performance data collected at both sites were used to assess the thermal behavior of the sections and to calibrate a thermal model. The results obtained from this study support the validity of the European recommendations and will be used to develop new optimized design procedures adapted for the Canadian context.
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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) | 0.001 | 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".