Performance of Permeable Pavements Under Low Volume Traffic Loads
Bibliographic record
Abstract
Most research on permeable pavements focused on the hydrological performance and was done primarily in mild climates, with little information available for the pavement structural response in cold climates. Hence, this research was commissioned in order to evaluate the performance of permeable pavements under low volume traffic loads in Calgary. The research facility located at the Currie Barracks in Southwest Calgary was divided into three individual cells (cell No.1 to 3) with pervious concrete pavement (PCP), permeable interlocking concrete pavement (PICP), and porous asphalt concrete pavement (PACP) as the wearing course, respectively. The highest and lowest stress response on the subgrade was recorded in the PACP and the PCP, respectively; with the exception of the frozen condition where the stress response for the PICP was slightly lower than the stress response for the PCP. The maximum stress response on the subgrade was recorded at the lowest speed of 5 kmph; while the stress response on the subgrade generally decreased with an increase in the traffic speed. The stress response was highest when the pavement structure was unsaturated; and lowest when the pavement structure was frozen. The findings indicated that the PCP showed the best structural performance with the exception of some ravelling. The impact of the frost heave was highest in the PICP. There was a significant increase in the surface deformation in the PICP subsequent to truck traffic for the flooded pavement structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".