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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".