On a semi-empirical approach to predicting sound insulation in lightweight framed construction
Bibliographic record
Abstract
Predicting the apparent airborne and impact sound insulation of lightweight framed constructions is very challenging, particularly if the approach is to be sufficiently simple for standardization. There are two basic approaches - semi-empirical or statistical energy analysis (SEA). The SEA approach is considered in an associated InterNoise 2007 paper entitled, 'Measurement and prediction of flanking transmission through gypsum board walls with modified SEA method'. This paper explores the strengths and weaknesses of the semi-empirical approach by considering flanking involving the wall/floor junction as an example. Flanking path power flow is defined by five transmission factors whose combined effect is characterized by a path transfer function specific to the type of excitation (airborne or impact) and the construction detail. For similar constructions, path estimates are obtained by adding a correction to account for the mounting, number, and type of layers of the flanking surface. Unfortunately, to deal with junction attenuation, a unique transfer function is required for each major type of structural framing at the junction (i.e., joist orientation and continuity). Relatively few transfer functions are required to accurately predict a large number of practical construction scenarios including the effect of toppings on floors and the mounting of the gypsum board on walls and ceilings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".