Analysis of sound radiation from the coupling effect of vibrating noise and moving-vehicle noise on bridges
Bibliographic record
Abstract
An acoustic finite element model of a bridge is developed to evaluate the noise generated by the traffic-induced vibration of the bridge. The dynamic response of a multi-girder bridge, modeled by a three-dimensional (3-D) frame element model, is analyzed with a 3-axle (8 degrees of freedom (DOF)) truck model and a 5-axle (13 DOF) tractor-trailer. The flat plate element is used to analyze the acoustic pressure due to the fluid–structure interactions between the vibrating surface and contiguous acoustic fluid medium. The radiation fields of noise with a specified distribution of vibrating velocity and pressure on the structural surface are also computed using the Kirchhoff–Helmholtz integral. Among the diverse parameters affecting the dynamic response of a bridge, vehicle velocity, vehicle weight, and spatial distribution of the road surface roughness are found to be the main factors that increase the level of vibration noise. In an attempt to illustrate the influence of the structural vibration noise of a bridge to total noise level around the bridge, the random function is used to generate the vehicle noise source including the engine noise and the rolling noise between the road and tire. The results show that the low-frequency noise produced by the vibrating bridge members amplifies the high-frequency vehicle noise by 4–7 dB. In addition, the amplification rate of noise increases with traveling speed and vehicle weight. Key words: acoustic pressure on surface, sound radiation, noise level, Kirchhoff–Helmholtz integrals, dynamic response, vehicle noise model, sound pressure level.
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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 | 0.001 |
| 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 | 0.001 | 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".