A Comparative Analysis of CLT Floor Systems Considering an Acoustic Prediction Model and Life Cycle Assessment (LCA)
Bibliographic record
Abstract
This article discusses a design methodology that considers the development of a sound insulation prediction tool, using an artificial neural networks, and life cycle assessment analysis. 112 sound insulation measurements are employed to develop the prediction model. They are lab-based measurements and are performed on 45 various CLT- and ribbed CLT-based assemblies. The acoustic model demonstrates satisfactory results with 1 dB differences in the prediction of airborne and impact sound indices (R?? and L??,??). An acoustic sensitivity study and a statistical analysis are then conducted to validate the model's results. Additionally, an LCA analysis is performed on the floor assemblies to calculate their environmental footprints. LCA categories are plotted against the acoustic performance of floors. No correlations are found, and the results emphasize that a wide range of sound insulation can be achieved with similar environmental impacts. Within each acoustic performance tier, the LCA results can be optimized for a floor assembly by selecting appropriate materials. Designers should be cognizant of the trade-offs between acoustic insulation, and environmental performance when choosing assemblies with favorable environmental impacts.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".