Effects of Seasonal Variation on the Load-Bearing Capacity of Pavements Composed of Insulation Layers
Bibliographic record
Abstract
Seasonal variation in the subgrade resilient modulus is likely caused by external factors, such as precipitation and freeze–thaw cycles. One of the strategies for minimizing the impact of this variation on the subgrade modulus is to use insulation layers to prevent frost penetration. This study investigated the effects of the use of insulation layers on pavement performance in the fully instrumented Integrated Road Research Facility in Edmonton, Alberta, Canada. Three insulated sections of the test road were comprised of bottom ash (BA) (100 cm) and polystyrene boards of two thicknesses (5 and 10 cm), and the adjacent conventional section was considered the control section (CS). The resilient modulus and the effective modulus of pavement were backcalculated with the data obtained from falling weight deflectometer testing conducted at the test road during a 1-year monitoring period, from July 2014 to July 2015. Temperature and moisture probes, installed across the depth of the sections, were used to determine the frozen, thawed, or recovering condition of the pavement. The study results revealed that polystyrene boards protected subgrade soil from freezing and thawing effects. The minimum ratio of the backcalculated subgrade modulus of each test to the resilient modulus of the test performed in September was 0.94 in the BA section, and the ratio of the CS could decrease to 0.88 in the recovering period. Comparison of the load-bearing capacity of insulated sections and the CS indicated that, unlike BA, polystyrene boards significantly decreased the load-bearing capacity of the pavement.
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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".