Improving Restorative Maintenance Practices for Mature Permeable Interlocking Concrete Pavements
Bibliographic record
Abstract
Permeable Interlocking Concrete Pavements (PICP) are a Low Impact Development (LID) technology that reduce the total volume of stormwater discharge and peak flows from urban hardscapes. Over time, particulates accumulate in the PICP joints, decreasing the pavement’s surface infiltration capacity and negatively affecting its overall functionality. Maintenance with two surface treatment technologies, a hand-held power brush and pressure washer-used in combination with vacuum street sweepers were compared to maintenance with vacuum street sweepers alone at four PICP parking lots. Both surface treatments along with vacuum street sweeping significantly improved the restoration of infiltration capacity for the young (i.e., <4 years) PICP section. Pressure washing in combination with vacuum sweeping was effective for PICP sections with larger (13–14 mm) joint openings. Power brushing, however, provided inconsistent results between the PICP sections. The effect of surface treatments was not significant for older (i.e., >6 years) installations with small (3–4 mm) joint openings. Though surface treatment resulted in significant improvement with a pressure washer and vacuum street sweeper combination, usage intensity of the parking lot was deemed as an important factor in restoring infiltration capacity. These findings re-emphasize that regular maintenance is essential to ensure long-term hydraulic functionality of PICP.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".