Sound Absorption Characteristics of Typical Pavements: An Ontario Study
Bibliographic record
Abstract
Traffic noise is a growing problem throughout the world. Pavement with high acoustic absorption capabilities can significantly reduce the roadway traffic noise. The durability of such acoustically absorptive pavements is however major concern for highway application. The sound absorption capabilities of typical portland cement concrete (PCC) pavements that were surface textured in different configurations and different asphalt concrete (AC) pavements, typical to Ontario (Canada), were measured in the Center for Pavement and Transportation Technology (CPATT) laboratory at the University of Waterloo, Ontario using the CPATT impedance tube and a custom designed portable reverberation chamber. On average, regular Superpave (SP), stone mastic asphalt (SMA) and fine graded SP mixes were shown to absorb 6.3%, 7.5%, and 8.5% of sound, respectively. Textured PCC surfaces were shown to absorb 5% to 6% of the sound. The varying thickness has shown no significant effect on the variation of sound absorption of conventional AC and PCC pavements. The variation of bulk relative density (BRD) was shown to affect significantly the sound absorption capabilities of conventional dense AC pavements. However, the effect of the air void contents in the dense AC pavements was shown to be insignificant or minimal for the variation of sound absorption capabilities of the tested AC pavements.
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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 | 0.000 | 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 | 0.001 | 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".