Wood or concrete floor? A comparison of direct sound insulation
Bibliographic record
Abstract
In the process of designing buildings, architects have to consider many aspects from different disciplines in order to choose the most appropriate materials and design details. Regarding only sound insulation, a 320 mm deep wood framed floor performs better for most sound insulation ratings than an eight times heavier 150 mm thick concrete floor, especially if a heavy topping on a resilient layer is added on both. This paper presents the results of a small study comparing the direct sound insulation of a wood frame and a concrete base floor (both bare and with a heavy topping), measured with four standardized sources (Airborne Sound and Tapping Machine according to ISO 10140 Parts 2 & 3 respectively, and Ball and Tire both according JIS 1418-2 and KS F 2810-2). As mentioned above, this study shows that with a topping the wood floor performs better than the concrete floor over all standard frequency ranges. It was also found that the improvement of sound insulation due to a topping is very similar for all sources except for the Tire or "Bang Machine", which is an outlier probably due to its extremely forceful impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".