Mitigation of Highway Traffic-Induced Vibration
Bibliographic record
Abstract
Occasionally, transportation agencies receive complaints from residents living near roads about annoying or even structurally-damaging traffic-induced vibration. The resolution of these complaints can be very challenging because many transportation agencies do not have guidelines for the assessment of the potential impact of traffic-induced vibration. Many agencies may also lack experience with dealing with vibration complaints, and with measures to mitigate the impact of traffic-induced vibration. The paper describes sources of traffic-induced vibration, identifies possible causes that may result in vibration concerns, and outlines procedures for estimating vibration levels caused by highway traffic. In addition, the paper provides guidelines and recommended criteria for the assessment of vibration impacts on residential areas, and provides recommendations for the mitigation of traffic-induced vibration. Both types of traffic induced vibration – ground-borne vibration and air-borne vibration – are addressed. The assessment of the impact of vibration can be accomplished by estimating the site-specific vibration levels and comparing them with assessment criteria and guidelines. The site specific factors influencing vibration levels include the characteristics of the highway traffic flow, unevenness of pavement surface, transmission path between the source and the receiver, and building parameters. In extreme
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 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".