A sound insulation prediction tool and LCA: A comparative study considering different wooden assemblies
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims to develop an acoustic design methodology for CLT floor assemblies using artificial neural networks approach by integration of life cycle assessment (LCA). 72 Lab-based measurements are used to develop the acoustic prediction tool. They are related to 29 different CLT-based floor assemblies. The weighted sound reduction index (Rw), and the weighted normalized impact sound pressure level (Ln,w) are estimated with an accuracy of 2 dB. Then a LCA study is conducted on assemblies that are used to test the network model. The acoustic performance and their environmental impacts are compared to highlight trends that may guide decision-makers in the design phase. This paper initially found that CLT-based floor assemblies generally increase the environmental impacts to achieve better acoustic insulation. However, a good sound attenuation can be reached by selecting suitable acoustic solutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it