Ground-Borne Noise and Vibration in Buildings Caused by Rail Transit
Bibliographic record
Abstract
Ground vibration produced by rail transit systems can be annoying to nearby building occupants when they perceive some combination of feelable vibration, re-radiated sound, and vibration-induced rattling of household paraphernalia. Community response to rail-induced ground vibration has not been extensively researched. While the well-known Schultz dosage-response curve is routinely used to predict the prevalence of annoyance produced by airborne transportation noise, no similar relationship has gained widespread acceptance for noise and vibration due to ground vibration. The principal goal of the present research was to develop a dosage-response relationship useful for predicting community annoyance due to ground vibration produced by rail transit systems. This report documents the research conducted under the Transit Cooperative Research Program D-12 project, including a literature review, development of the study design, conduct of telephone interviews and vibration measurements, and data reduction and analyses. Telephone interviews were conducted with 1306 individuals in five North American cities: New York, Sacramento, Dallas, Toronto and Boston. Field measurements were made in each city to estimate vibration and noise exposure at each interview location. The work produced several dosage-response relationships between vibration/noise exposure and annoyance. When compared to the current noise and vibration criteria specified by the Federal Transit Administration (FTA), the dosage-response analysis predicted a probability of 0.05 to 0.10 that a D-12 respondent would be highly annoyed by vibration and noise at the current FTA criterion levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".