Evaluation of pavement load bearing capacity comprised of insulation layers during thaw season
Bibliographic record
Abstract
One of the strategies for minimizing the negative effects of freezing on frost-susceptible subgrade is placing insulation layers on top of the subgrade. This technique helps to mitigate the formation of ice lenses and frost heave in the subgrade and subsequently reduces the associated damage. Using insulation layers prevents subgrade strength reduction that results from the existence of excess water formed from melted ice lenses during thaw season. This paper evaluates the effect of using bottom ash, a recently introduced by-product of power generation, as an insulation layer, as well as the effect of commonly used polystyrene boards on subgrade resilient modulus variations during thaw season at the University of Alberta’s IRRF test road facility in Edmonton, Canada. To evaluate the subgrade strength, Falling-Weight Deflectometer (FWD) testing was conducted at 10-m intervals along the test road during thaw season. The back-calculated moduli from deflection basins were used to determine the resilient modulus of the insulated sections and the control section. The study results revealed that using polystyrene boards as insulation layers protected the subgrade soil from freezing and thawing effect; however, it also reduced the pavement bearing capacity. The bottom ash layer was affected by freeze-thaw, but it could properly protect the subgrade soil without having an adverse effect on pavement bearing capacity.
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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 | 0.001 |
| 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) | 0.002 | 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".