A hybrid test method for measurement of airborne sound transmission loss of building partitions and elements
Bibliographic record
Abstract
Single-number ratings, such as sound transmission class (STC), outdoor to indoor transmission class (OITC), and weighted sound reduction index (Rw), have been widely used to indicate the sound transmission properties of the building elements. The required transmission loss values over the frequency spectrum can be obtained from the two reverberation rooms test method (ASTM E90, ISO 10140), field measurement method (ASTM E966), or intensity method (ISO 15186). The limitations of the number of testing facilities that can provide the diffused field, minimum cut-off frequency, and other standard laboratory requirements, made the sound transmission loss measurement a costly and time-consuming procedure. In this study, alternative methods to measure the sound pressure and intensity have been investigated. The main objective of this research was to eliminate the complicated requirements of the diffused field and the room absorption measurement by averaging the direct sound amplitude at several angle of incidents and using the intensity probe to measure the sound intensity. In conclusion, the sound transmission loss, STC, and OITC values of double-glazed windows obtained from the hybrid method have been compared with predictions from the existing theory and the ASTM E90 standard test results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".