Bibliographic record
Abstract
This study investigates safety performance of permeable friction courses (PFCs) in terms of hydroplaning. An analytical model was derived to calculate water depth on PFC under static rainfalls. A tire–water–pavement interaction model was used to predict hydroplaning speeds. The results show that water film depth increases from innermost to outermost traffic lanes. PFC can mitigate hydroplaning risk under 0.5 cm/h rain rate. At rain rate of 1 cm/h, the impact of horizontal hydraulic conductivity and PRF thickness on hydroplaning speed is less than 10%, but becomes negligible at higher rain rates. On the other hand, the flow slope of PFC significantly affects hydroplaning speed by over 50%, while this effect decreases as rain rate increases. The surface macrotexture of PFC shows less than 5% impact on hydroplaning speed at all rain rates. An analysis framework with design example is proposed to incorporate hydroplaning speed in decision-making of roadway design with PFC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".