Scale-model study of the effectiveness of highway noise barriers
Bibliographic record
Abstract
A scale-model facility was developed to test the insertion loss (IL) of highway noise barriers. Three model materials were utilized to simulate packed-earth berms and ground (expanded polystyrene), vertical walls (dense polystyrene), and roadways (varnished particleboard). Thirty-eight noise-barrier configurations were tested and used to compare how IL varied with changes to the barrier profile for walls, berms, and combinations of walls and berms for receivers at a representative, highway-adjacent location. The atmospheric conditions were assumed to be homogeneous and nonrefracting. Changes of barrier surface impedance were also assessed. A highway line source was simulated by positioning both an air-jet point source and a receiver microphone at a series of equally spaced points, in order to form an array of source-receiver measurement pairs making differing angles of propagation to the noise-barrier crest line. The IL measurement results are presented in unweighted third-octave bands. In addition, total A-weighted insertion losses (ILA) were obtained by applying an A-weighted, traffic-noise spectrum. When a berm was modeled with surface impedance closely matching that of packed earth, it was found that walls outperformed berms by 1 to 2 dBA. When the surface impedance of a berm was modeled to be acoustically soft, the ILA increased sufficiently to favor berms by about 2 dBA. The result for an acoustically soft berm does not support the long-standing practice of assuming that earth berms outperform walls by 3 dBA, but is consistent with the performance predicted by newer prediction algorithms. When the slopes of berms were made shallower, the IL generally decreased for a berm alone, but generally increased in cases with a wall atop the berm.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".