Apparent Impact Sound Insulation Performance of Continuous Floating Concrete Toppings on Mass Timber Slab Floors
Bibliographic record
Abstract
Mass timber panels including cross-laminated timber (CLT), dowel-laminated timber (DLT), and nail-laminated timber (NLT) are used increasingly as floor slabs in mass timber buildings and hybrid timber buildings. The sound insulation performance of bare mass timber structural floors is insufficient due to their lightweight and relatively high bending stiffness. Floating concrete toppings are commonly applied for improved sound insulation performance with an elastic interlayer. The effect of different elastic interlayers and thickness of concrete toppings on improving the impact sound insulation performance was investigated experimentally according to ASTM standards in this study. The results showed that with the same elastic layer, thicker concrete toppings resulted in better impact sound insulation performance with a higher apparent impact insulation class (AIIC). However, by increasing the concrete thickness from 38 to 50 mm and to 70 mm, the improvement of AIIC between two thicknesses was only within 3, and a significant improvement up to 9 was observed with a 100-mm-thick concrete topping. In general, elastic interlayers with lower dynamic stiffness values performed better; however, the performance was product dependent though the apparent dynamic stiffness was measured using the same standard method. Moreover, with the same panel thickness and wood species, a bare DLT floor provided higher AIIC (35) than a bare CLT (21), but each mass timber floor with the same interlayer and floating concrete topping had the same impact sound attenuation performance. The ISO empirical prediction equation overestimated the impact sound attenuation of floating concrete toppings on mass timber floors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".