Development of a new acoustic prediction tool by integration of life cycle assessment
Bibliographic record
Abstract
Recently, environmental awareness has been the key driver for using renewable materials that have low environmental impact and fulfill constructional requirements, such as timber. Despite the advantages of wood as a building material, it has a lower subjective quality of sound insulation. To fulfill the sound insulation requirements, it is, therefore, unavoidable to complement based-wooden assemblies with additional element(s). However, identifying the acoustic performance is costly and time-consuming. Therefore, developing an accurate prediction tool is vital. Since wood-based structures have been developed to consider the environmental aspects, the environmental performance of buildings should be integrated into the acoustic design. This paper aims to develop an acoustic design methodology for wooden structures using artificial neural network approach by integration of life cycle assessment (LCA). Various Lab-based measurements are used to develop the acoustic prediction tool. Then, a LCA study is conducted on the test assemblies. This paper initially found that wooded assemblies generally increase the environmental impacts to achieve better acoustic insulation. Moreover, different assemblies can meet the sound insulation requirements. Therefore, designers should cognize of environmental and acoustic trade-off by selecting assemblies that consider both aspects.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".