Comprehensive assessment of friction characteristics and durability of common pavement markings and materials using three-wheel polishing
Bibliographic record
Abstract
Pavement markings are vital for organizing traffic flow and enhancing safety, but their friction characteristics are often overlooked and insufficiently addressed. This study assessed the friction performance of six marking types used by the Indiana Department of Transportation: waterborne paint, preformed tape, epoxy, polyurea, methyl methacrylate, and thermoplastics. Specimens incorporating various materials, including binders, glass beads, and antiskid particles, were conditioned using a three-wheel polishing device to simulate traffic wear. Friction metrics were measured with the British pendulum tester, dynamic friction tester, and circular track meter. Results revealed freshly applied markings exhibit 44% higher friction in dry than wet conditions, with waterborne paint outperforming non-waterborne paints. Also, correlation analyses were conducted to examine the influence of bead type, application rate, and binder thickness on friction and durability. Findings improve understanding of marking materials’ performance, supporting the development of enhanced standards and maintenance practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".