Pavement surface friction and noise: integration into the pavement management system
Bibliographic record
Abstract
Pavement surface friction is an essential attribute of highway safety. The desired surface friction should also accommodate the seasonal and long-term variations. The available surface friction depends mainly on microtexture and macrotexture on pavement surfaces. An increased surface texture for increased and durable friction may increase the roadway noise, which is also a growing problem. Traditional sound barriers, constructed to reduce noise exposure, are very expensive. As the pavement surface characteristics play a key role in roadway noise generation, it provides a window for noise reduction by altering the pavement surface. The challenge, however, is to provide a smooth, quiet, long-lasting, and economic pavement with adequate and durable surface friction. This paper addresses this challenge and provides a realistic guideline. The correlation of tire–road noise and surface friction is examined using data collected from five different asphalt pavements. Frameworks for desired minimum surface friction and maximum roadway noise are provided. A modified value-engineering approach is proposed to accommodate the construction and maintenance costs, longevity, smoothness, safety, and noise in the selection of pavement surfaces.
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 | 0.001 | 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".