Key Insights into Permeable Interlocking Concrete Pavement: Measuring, Mimicking and Mitigating Clogging
Bibliographic record
Abstract
Permeable Interlocking Concrete Pavements (PICPs) are a Low Impact Development technology designed to mimic natural pathways and control rainfall events at the source. As these pavements age, sediments accumulate within their permeable joints, causing them to clog and lose their capacity to infiltrate stormwater. Maintenance of PICPs is a critical practice to ensure long-term functionality. This thesis evaluates restorative maintenance practices for PICP and investigates how sediment characteristics influence clogging processes. To achieve this goal, a novel accelerated clogging method was developed, tested and applied in both field and laboratory experiments. Standardizing clogging methodologies will allow researchers to translate laboratory results more easily into actionable maintenance plans for operational pavements and allow for direct evaluations of new maintenance technologies. The accelerated clogging method was used to evaluate different restorative maintenance techniques, including three street sweeper technologies, a specialized high pressurized air and vacuum technology intended specifically for PICP maintenance and manual pressure washing followed by vacuuming. Each technology restored infiltration rates to above 2,000 mm/hr; however, only the specialized equipment restored infiltration rates to post-construction conditions. When the specialized equipment was re-tested for maintenance capabilities on a surface clogged with on-site (cohesive) soils, it could only restore infiltration rates to approximately 50% post-construction conditions. Lastly, the regenerative air sweeper was retested for early and repeated maintenance, which increased the effective lifespan of the PICP but was unable to prevent eventually clogging of the surface. In a parallel laboratory study, PICP clogging and surface infiltration losses were observed when varying material gradation and type using the same accelerated clogging procedure and exposing the surfaces to cycled wetting and drying. PICPs cells that were clogged with well-graded and cohesive materials lost their surface infiltration rates more rapidly compared to cells clogged non-cohesive or poorly graded materials. Well-graded clogging material allowed coarse sediments to cause early blockages and fine sediments to accumulate near the surface. Simultaneously cohesive sediments agglomerated together, forming a surface crust and attraction to water, limiting the amount of water permeation through the clogged joints leading to decreased infiltration rates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".