Long Term Drainage Performance of Pervious Concrete Pavements in Canada
Bibliographic record
Abstract
Pervious concrete pavement is an eco-friendly pavement system which can offer various and sustainable benefits for stormwater management. It can be considered as an alternative to impervious pavement system as the open void structure of pervious concrete pavement allows water to infiltrate very quickly through it and join the natural ground water. Though pervious concrete pavement has been used in parts of Europe and the southern United States for many years, the practice of using it in northern cold climates such as Canada is more recent. Several pervious concrete pavement field sites were constructed by the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo, the Cement Association of Canada, and several other industry members. Initial results from this work have been published previously, and include the performance analysis, permeability evaluation, and strength assessment. However, collecting drainage characteristic data from instruments such as the moisture gauge measurements, strain gauge at three sites have continued to be monitored. This field/laboratory study is providing insight into the short and medium term drainage performance of pervious concrete pavement. As a continuation of previous work, this paper discusses the effect of mix design in the performance of pervious concrete, the long term drainage instrumentation performance that have been obtained from instrumentation at sites in British Columbia, Ontario and Quebec. An analysis framework is also presented in this paper. The findings from this paper will provide useful information for designers and practitioners on the long term drainage performance of pervious concrete pavement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".