Blocking Mass For Architectural Vibration Attenuation – A Case Study
Bibliographic record
Abstract
With many cities facing the challenges of urban infill, a large number of older buildings are being altered to host activities which are not necessarily compatible with the original intent of the building design. As such, noise-sensitive operations are sometimes forced into close quarters with inherently noisy neighbouring tenants/owners. This case study reports the acoustic, noise and vibration challenges associated with the retrofitting of an old theatre which was repurposed and split into two adjacent properties: a large music venue on one side, and an off-track betting facility on the other. Specifically, this paper addresses the continuous metal roof deck between both operations, which constitutes one of the primary noise flanking paths. Noise energy from the music venue excites the common roof deck, allowing vibration to travel across the demising partition and into the off-track betting facility, where it is re-radiated as structure-borne noise at the point of reception. In order to help mitigate this issue, a blocking mass was designed to reflect roof deck vibrations back towards the music venue. While these blocking masses are typically installed on top of the roof deck, design constraints resulted in the suspension of the blocking mass from the underside of the roof deck. The predicted vibration attenuation is discussed, along with the proposed design which combines the blocking mass with the demising partition, effectively blending these two sound attenuating elements together.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".