Acoustics in buildings with mass timber floor/ceiling assemblies: theory, practice and acoustical solutions.
Bibliographic record
Abstract
There has been much information previously published regarding the sound isolation performance of cross laminated timber (CLT) construction, particularly relating to sound transmission (airborne and impact) through different types of bare panels, and panels in combination with supplemental construction elements (or ‘linings’) and the influence of construction detailing on sound flanking. This paper focusses on some ‘secondary’ acoustical design challenges when using typical acoustical (non-CLT) materials within a CLT building structure, such as maintaining appropriate levels of room-to- room sound isolation, given the irregularly sized gaps that sometimes occur between CLT panels at the underside of mass timber floor/ceiling assemblies. Other challenges lie in the accurate prediction of the influence of sound flanking paths on room-to-room sound isolation in such structures and in finding methods for adding acoustically absorbent finishes to a space, where functionally required, when the project stakeholders wish to see timber left visually exposed. This paper reviews some of our company’s acoustical design experience for projects constructed within buildings utilizing CLT construction. These projects located in Canada and the USA, include residential, office buildings and even sound studio projects. Strategies are reviewed that have been used successfully to address potential acoustical challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".