Mitigation of Railway Induced Ground-borne Noise and Vibration
Bibliographic record
Abstract
New surface and underground railway lines are increasingly being introduced into urban areas as a result of intensification. Furthermore, new developments are being built with lighter construction materials and longer spans in close proximity to track alignments. These factors have led to more ground-borne noise and vibration being perceived inside buildings and increased potential for annoyance for the occupants. Vibrating walls and floors act like giant loudspeakers reradiating the acoustic energy as noise. Low levels of vibration, even below the level of human perception can interfere with the operation of sensitive equipment found in hospitals, labs and high-tech facilities. There are alternative methods to mitigate the impact of ground borne noise and vibration from railway traffic which can be used individually or together: isolating the source, interrupting the vibration path and/or isolating the receiver. This paper reviews the common mitigation methods of each type available to the design community. Increasing track compliance by introducing resilient elements in the rail support system can reduce transfer of vibration to the ground. Vibration can be prevented from reaching buildings through properly built in-ground wave barriers acting in an analogous manner to sound barriers. Introducing resilient element in building foundations can isolate entire buildings from vibration. Sensitive equipment can be mounted on special isolating tables or mounts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".