A Design Methodology Incorporating a Sound Insulation Prediction Model, Life Cycle Assessment (LCA), and Thermal Insulation: A Comparative Study of Various Cross-Laminated Timber (CLT) and Ribbed CLT-Based Floor Assemblies
Bibliographic record
Abstract
Mass timber is increasingly being employed in constructing low- and mid-rise buildings. One of the primary reasons for using mass timber structures is their sustainability and ability to reduce environmental consequences in the building sector. One criticism of these structures is their lower subjective sound insulation quality. Therefore, acoustic treatments should be considered. However, acoustic solutions do not necessarily contribute to lower environmental impacts or improved thermal insulation performance. This paper discusses a design methodology that incorporates the development of a sound insulation prediction tool (using an artificial neural networks approach), life cycle assessment analysis, and thermal insulation study. A total of 112 sound insulation measurements (in one-third octave bands from 50 to 5000 Hz) are utilized to develop the network model and are also used for the LCA and thermal insulation study. 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 (Rw and Ln,w). 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. The thermal insulation of floors is then calculated. Overall, a strong positive correlation is found between the total thermal resistance and heat loss against acoustic performance. Designers should be cognizant of the trade-offs between acoustic, thermal insulation, and environmental performance when choosing assemblies with favorable environmental impacts relative to acoustic and thermal insulation ratios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".