An Accelerated Clogging Method by Manual Application of Sediments for Permeable Interlocking Concrete Pavements
Bibliographic record
Abstract
Abstract The primary function of permeable interlocking concrete pavements (PICP) is to allow water to rapidly infiltrate through the surface and percolate to underlying sublayers, soils, or underdrains. However, as pavements age, surface infiltration rates diminish as sediments and debris accumulate on the pavement surface, eventually leading to surface ponding and runoff if no maintenance is applied. These studies clog PICP surfaces to better understand clogging mechanisms under different sediment types and to evaluate maintenance technologies intended to restore surface permeability. Clogging studies use different materials and methods to clog PICP surfaces, which impedes comparisons and interpretations across projects. This paper presents a new methodology to (1) create a realistic synthetic clogging material and (2) rapidly clog a PICP surface for experiments. The method described herein can be easily repeated by others to test a broad range of research questions for PICP surfaces while producing comparable results for studies conducted at different scales (lab, meso, field) and locations or with different sediment mixes. The proposed method was used to clog six identical PICP meso-cells in the field. Five cells were clogged with graded street sweepings collected from local municipal waste yards, and a sixth cell was clogged with a mixture of street sweepings and soils collected on-site. Over five weeks, the PICP surface infiltration rates were reduced from postconstruction levels (>10,800 mm/h) to approximately 250 mm/h. Statistical spatial analysis indicates that the surfaces were clogged evenly, but repeating infiltration measurements at the same location was observed to influence the resulting surface infiltration data. The PICP surface clogged with mixed street sweepings and on-site soils required approximately 43 % less material than surfaces clogged with street sweepings only to reduce surface infiltration rates to 250 mm/h.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".