Modeling of Field Sound Insulation for Multi-Layered CLT Floor Assemblies Using Artificial Neural Networks
Bibliographic record
Abstract
A means prediction tool based on an artificial neural network approach is developed to predict standardized level differences and standardized impact sound pressure levels for multi-layered CLT-based floor systems. The collected data are 104 field sound insulation measurements in one-third-octave bands from 50 Hz to 5 kHz. The acoustic measurements were implemented in 15 different buildings in Europe and for different room functions and sizes. Various structural parameters were organized to develop the network model, such as floor components, surrounding wall types and their components, junction types and their visco-elastic interlayer, receiving room volume, surface separating area, and more. The developed network demonstrates satisfactory results in predicting standardized field airborne and impact sound insulation curves across all frequencies. The weighted standardized level differences DnTw are estimated with 1 dB variation, while up to 2 dB for standardized impact sound pressure level L0 nTw. A good correlation is highlighted for airborne estimations in the middle frequencies (200 - 1000 Hz), while higher frequencies often reveal some deviations. However, impact insulation estimations showed better accuracy in the high-frequency range (1.25 - 5 kHz).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".