A COMPARISON OF NUMERICAL APPROACHES TO QUANTIFY SOUND INSULATION OF LIGHTWEIGHT FLOOR STRUCTURES
Bibliographic record
Abstract
Quantifying air-borne and structure-borne sound insulation is an important design consideration for the indoor comfort in a building.Although sound insulation performance is commonly measured experimentally, numerical methods can have time-saving and economic benefits.Further, numerical methods can be incorporated within building simulations to provide an estimate of the acoustic environment.In response, this paper evaluates three different computational approaches for quantifying sound insulation in one-third octave bands (50 Hz -5 kHz) of a lightweight floor including: an analytical (theoretical) model, a finite element model (FEM), and an artificial neural network (ANN) model.The three numerical methods are tested on the sound insulation of a cross laminated timber (CLT) floor.The results of this study show that the ANN model is able to accurately predict the air-borne and impact sound insulation performance at frequencies above 1 Mohamad.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".