SOUND INSULATION OF LIGHTWEIGHT WOODEN FLOOR STRUCTURES ANN MODEL AND SENSITIVITY ANALYSIS
Bibliographic record
Abstract
The study aims to develop an artificial neural networks (ANN) model to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors.The prediction model is developed using 252 standardized laboratory measurement curves in one-third octave bands (50 -5000 Hz).Each floor structure has been divided into three parts in the database: upper, main and ceiling parts.Physical and geometric characteristics (materials, thickness, density, dimensions, mass, and more) are used as network parameters.The results demonstrated that the predictive ability of the model is satisfactory.The forecast of the weighted airborne sound reduction index R w was calculated with a maximum error of 2 dB.However, it is increased up to 5 dB in the worst case prediction of the weighted normalized impact sound pressure level L n,w .A sensitivity analysis explored the essential parameters on sound insulation estimation.The thickness and the density of upper and main parts of the floors seem to affect estimations the most in all frequencies.In addition, no remarkable attribution has been found for the thickness and density of the ceiling part of the structures.
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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 | 0.000 |
| 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".