ACOUSTIC SENSITIVITY ANALYSIS AND MODELING OF SOUND INSULATION PERFORMANCE OF LIGHTWEIGHT WOODEN FACADE STRUCTURES
Bibliographic record
Abstract
A prediction model is developed based on neural networks approach to estimate the air-borne sound insulation performance of lightweight wooden fac ¸ade walls.A hundred insulation curves are used to develop the model, and they are lab-based measurements performed on various fac ¸ades in one-third-octave bands (50 Hz-5 kHz).For each wall, geometric and physical information (material types, dimensions, thicknesses, densities, and more) are used as input structural parameters.The results are satisfactory, and the model can estimate air-borne sound reduction with acceptable variations.A better estimation is achieved at middle frequencies (250 Hz-1 kHz), while lower and higher frequency bands often depict higher deviations.The weighted air-borne sound reduction index (R w ) can be forecast with a maximum error of 3 dB.In certain cases, the model shows high deviations within fundamental and critical frequencies, which influence the predictive precision.A sensitivity analysis is implemented to investigate on which structural parameters the model relies.The results emphasize the importance of fac ¸ade thickness and the total density of the clustered exterior layers.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".