Performance of Pervious Concrete Pavement in Freeze-Thaw Conditions and With Winter Maintenance
Bibliographic record
Abstract
In 2007 the Cement Association of Canada, industry members and the Centre for Pavement and Transportation Technology at the University of Waterloo partnered to carry out a study to evaluate the behavior of pervious concrete pavement in the Canadian climate. Field sites have been constructed and monitored and laboratory testing has also been carried out on pervious concrete pavement. This paper focuses on the behavior that has been demonstrated by pervious concrete in the laboratory when exposed to two factors: freeze-thaw cycling and moisture; and winter maintenance. The behavior was assessed in terms of permeability and surface distress development. Freeze-thaw cycling and moisture proved to on occasion alter the interior structure of the pervious concrete pavement. The extent to which this occurs is deemed to be related to the paste characteristics. Salt solution as a winter maintenance technique was found to be very damaging to the pervious concrete samples. Sand as a winter maintenance method led to minimal surface distress development. Permeability was decreased with the use of sand and salt solutions as winter maintenance techniques; however, remained adequate with both. This laboratory research presents results that should be applied to field sites for verification in full scale scenarios. In general the presence of freeze-thaw cycles, moisture and winter maintenance did not lead to poor performance of the pervious concrete pavement in the laboratory.
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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) | 0.000 | 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".