Investigation of Sound Transmission through Floors: A Correlation of Data
Bibliographic record
Abstract
The proposal of this thesis is to examine existing data on floor impact studies, in particular for heavy-weight or concrete flooring, and see if a correlation can be drawn linking floor covering types to resultant levels of impact noise in the room below, as produced by a standard tapping machine.The main aim is to correlate data, primarily from Dr. Alf Warnock's studies in Canada, into a database, and then to see if any conclusions can be drawn on the impact noise levels made. Data was collected identifying the thickness of concrete slab tested, type of underlay used, and floor finish type. Following this collection and comparison of data, testing was done to add to these results and to verify the levels attained as being accurate. Tests were completed at a building site, testing a few different thicknesses of rubber underlay.Construction of the database in Microsoft Access and Excel yielded some interesting data. Issues were identified with respect to reproducibility of results and therefore the validity with which results from different tests could be accurately compared. It was determined that the best way to compare results would be to compare results within the one test between the bare slab and with the floor covering. This would give an idea of the reduction in noise level that could be expected. By comparing similartests (i.e. with a similar floor topping), and comparing the relative differences between the bare slab floor and with topping, would give an idea of the amount of reduction to expect from a specific floor type.The experimental testing that was carried out demonstrated this to be a feasible procedure. Results obtained from testing gave a similar relative difference between product types as revealed in the database. This thesis has been able to determine that there is a reliable way to compare results from different tests and therefore predict an approximate noise reduction value for various floor assemblies.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".