Pavement Surface Mixture, Texture, and Skid Resistance: A Factorial Analysis
Bibliographic record
Abstract
Skidding on wet pavements contributes to a substantial portion of highway crashes. The resistance to skidding however depends on the microtexture and macrotexture available on pavement surfaces. Several past studies have focused this aspect but with inadequate or inconsistent conclusions. The uniqueness of this study is that surface texture performance has been evaluated controlling the variability in aggregate mineralogy, environmental condition, construction and service. Portland cement concrete (PCC) specimens were prepared from a single mixture to evaluate the true effect of various surface textures on friction properties. Asphalt concrete (AC) surfaces with the same construction record were tested to examine the true effect of mix properties on surface texture and friction. Pavement surface texture was measured using the sand patch method and Automated Road Analyzer (ARAN) while the skid resistance was measured using the British Pendulum and skid trailer. Analysis has shown that a Mean Texture Depth (MTD) of about 1.8 mm is the optimum macrotexture for maximum surface friction on textured concrete surfaces. Exposed aggregate concrete may not be a preferred texture because of the benefit of sand microtexture is lost with washing out of surface mortar. AC surfaces with complex macrotexture have shown to perform differently from PCC surfaces with simple macrotexture pattern. The hypothesis that British Pendulum Number (BPN) is dependent only on surface microtexture and represents low speed friction has appeared to be invalid. The skid number-speed gradient is not something universal but varies from mix to mix. Several statistically significant models have also been developed correlating the skid resistance with the asphalt mix grading composition and surface macrotexture and with concrete surface macrotexture.
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".