Investigation of Water Drainage Capability for Porous Asphalt Material with Varying Slope and Porosity Based on Laboratory Experiment
Bibliographic record
Abstract
This study assessed the water drainage capability of porous asphalt material (PAM) based on laboratory rainfall simulator tests. A series of tests were conducted using a range of slopes and rainfall intensities. The results showed that as the rainfall intensity increased, the water subsurface drainage steadily decreased. When the slope increased, the subsurface outflow of the porous asphalt decreased. Nevertheless, the slope of PAM insignificantly affected the water subsurface drainage. For the PAM specimen with a porosity of 15%, at a rainfall intensity of 2.5 L/min, when the slope increased from 0% to 8%, the subsurface outflow reduced by 2.8%. The investigation of the effect of porosity on subsurface drainage showed that the porous asphalt with a higher porosity displayed a higher subsurface drainage. At the slope of 4%, at a rainfall intensity of 4.9 L/min, for the PAM specimen with porosity of 10% and 15%, the subsurface outflow was 72.3% and 79.1%, respectively. It could be seen that the porosity had a strong effect on the drainage capability of PAM. The above results imply that the utilization of PAM depended more on the porosity than the slope. In the future, further experiments evaluating the water drainage of PAM should be adopted.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".