Novel Operation and Maintenance Practices for Permeable Pavements
Bibliographic record
Abstract
This thesis investigates several opportunities for operation and maintenance to improve the performance of permeable pavements by testing strategies to rejuvenate old pavements, improve winter stormwater quality, and enhance stormwater infiltration. Testing of pre-treatment maintenance followed by street sweeping on mature permeable interlocking concrete pavement parking lots in 2016 revealed that pre-treatment with pressure wash significantly improves the effectiveness of vacuum sweeping. Pavement age and usage were found to affect the restoration of surface infiltration significantly. Older pavements and high traffic areas were considerably more challenging to restore for both control and test groups. Power brushing gave inconclusive results across test groups. Chloride concentrations in surface runoff and zero-exfiltration permeable pavement effluent were measured between Jan 2016 to May 2017 and compared with concentration in runoff from a conventional asphalt pavement. Runoff from asphalt generated large spikes in chloride concentration over 21,800 mg/L. However, when combined with the flashier runoff hydrograph, these high instantaneous chloride levels, lasting a few hours, resulted in less exposure to elevated concentrations. Permeable pavements, in contrast, attenuated peak chloride concentrations during winter. Furthermore, chloride was retained only temporarily and flushed from the pavement reservoir during spring melt, suggesting that attenuated chloride release from permeable pavement effluent is unlikely throughout the summer or fall months. Smart adaptive operation of PICP underdrains was tested to increase stormwater infiltration on low permeability soils. This unique study is the first demonstration of ‘smart’ operations for permeable pavements. The three-year evaluation, 2016-2018, confirmed that extended detention of stormwater could allow permeable pavements to control and infiltrate excess stormwater run-on and increase the probability of managing extreme events in non-run-on situations. The study also noted that high volume reductions could be achieved even when low permeability soils are present on site. Overall, the thesis bridges the gap between theory and practice by guiding how to improve operation and maintenance techniques for permeable pavements. This research aims to benefit permeable pavement manufacturers, suppliers, and installers in Canada by highlighting opportunities to improve permeable pavements' environmental benefits and management practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.003 | 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 teacher head, 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".