Exploring acoustical approach for pre-screening of building envelope airtightness level
Bibliographic record
Abstract
Air infiltration plays a significant role in designing and evaluating the performance and air quality of a building. Air leakage through an existing building enclosure can both be localized and calculated by using experimental measurement, such as blower door test system, tracer gas method, and transient approach. Estimating building air permeability through these methods can be expensive, time consuming, and weather reliant. The economical and environmental effect of air infiltration through building envelope leads towards more research on detecting air leakage locations and estimating air infiltration rate through newly introduced techniques, such as using an acoustical method. As such, in this research, a general review of airtightness detection and quantification method will be presented, especially acoustical technique which will be explored more deeply. Moreover, due to significant impact of window systems on total air infiltration through building envelope, the correlation between sound transmission loss and air permeability through seven window assemblies in an existing building will be explored in order to investigate acoustical method further. In addition, the acoustic air leakage detection method based on standard ASTM E1186 will be instigated on the experimental windows. The results reveal the negative relationship between these two phenomena.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".