Complexities of Curtain Wall Flanking Transmission – A Case Study
Bibliographic record
Abstract
Curtain wall construction is very common in modern commercial buildings. The continuous external facade can lead to significant limitations on the sound isolation between adjacent rooms, separated either laterally or vertically. Flanking transmission of curtain wall systems is very difficult to predict at the design stage of a project. Manufactures do not routinely measure this parameter. The scarcity of data is partly due to a lack of laboratories that have the necessary specialized test environment. Generic flanking transmission loss data is of little value because of the large variation in curtain wall assemblies. Designers are often left with best guess approximations based on previous experience. These factors also make it difficult to significantly improve flanking transmission of an existing curtain wall installation. This paper describes a project where significant flanking along the curtain wall resulted in poor sound isolation between floors. Due to the complexity of the junction between the curtain wall and the floor structure, several potential flanking paths were identified and evaluated. It was possible to alter each flanking path individually so that the incremental improvement of each step could be quantified. After implementing several modifications, an improvement of approximately 15 dB was observed across a wide frequency range.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".