Concrete pavement surface textures and multivariables frictional performance analysis: a North American case study
Bibliographic record
Abstract
Concrete pavements are well known for long structural life. The major challenge, however, is to provide a durable surface with adequate skid resistance for economy and safety. This paper examines the long-term frictional performance of eight different surface textures on 197 sections of concrete pavements within the long-term pavement performance (LTPP) program database. Analysis shows that tined and (or) grooved textures maintain consistently higher skid resistance over time and the surface friction of concrete pavements is less sensitive to ambient temperature. Cumulative traffic passes are more sensitive to longevity of friction than the cumulative axle loads. Five alternative models have also been successfully developed for prediction of the long-term skid resistance of concrete pavements as a function of texture type, cumulative traffic passes, speed, and concrete compressive strength. These models were shown to be statistically significant at 95% confidence levels with reasonable prediction accuracy.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".