Determining Surface Infiltration Rate of Permeable Pavements with Digital Imaging
Bibliographic record
Abstract
Cell phone images of pervious pavement surfaces were used to explore relationships between surface infiltration rates (SIR) measured using the ASTM C1701 standard test and using a simple falling head test. A fiber-reinforced porous asphalt surface and a highly permeable material comprised of stone, rubber and a polymer binder (Porous Pave) were tested. Images taken with a high-resolution cellphone camera were acquired as JPEG files and converted to gray scale images in Matlab® for analysis. The distribution of gray levels was compared to the surface infiltration rates obtained for both pavements with attention given to the mean of the distribution. Investigation into the relationships between mean SIR and parameters determined from the gray level distribution produced in the image analysis revealed that mean SIR measured in both pavements were proportional to the inverse of the mean of the distribution. The relationships produced a coefficient of determination over 85% using both the ASTM and the falling head test in the porous asphalt surface. SIR measurements determined with the ASTM method were highly correlated with the inverse mean of the distribution of gray levels in the Porous Pave material as well, producing coefficients of determination of over 90% and Kendall’s tau-b of roughly 70% for nonparametric data.
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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 | 0.001 |
| 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) | 0.002 | 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".