Acoustic design tools for estimation of sound insulation performance of wood wall and floor assemblies
Bibliographic record
Abstract
The National Building Code of Canada 2015 stipulates the minimum requirements of the airborne sound insulation transmission through common interior walls and ceiling/floor assemblies. The required minimum Apparent Sound Transmission Class (ASTC) is 47 in Canada, whereas the Impact Insulation Class (IIC) for floors is recommended to be higher than 55. For many years, significant efforts were made to develop sound insulation prediction models or tools to predict the sound insulation performance of wall and floor/ceiling assemblies at the design phase in order to meet the requirements and the recommendations made by codes. However, today few models can provide a reliable acoustics design tool. In this document, three prediction tools thought to be practically useful are presented and evaluated. Between these three prediction tools, one is an analytical model of the Insul software while the other two are empirical models developed by the National Research Council of Canada and the American Wood Council. This paper compared the STC and IIC ratings of wood wall and floor assemblies estimated by these three models and verified them by the measured STC and IIC ratings. This work aims at providing an idea for readers to choose a suitable design tool to proceed with their acoustic designs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".