Unlock the myth of low-frequency footstep thumping noise in lightweight wood floors
Bibliographic record
Abstract
Major occupants' complaints received in lightweight wood floors of Impact Insulation Class (IIC) above 55, are low-frequency footstep thumping noise. Lack of understanding of fundamentals and solutions motived this study. A series experiments were conducted to answer the following questions: 1) what are the frequency range of the low-frequency footstep thumping noises heard by the occupants in mass timber slab floor and in light frame wood joisted floor when the floors are impacted by the ISO tapping machine, footsteps of a person walking with shoes, and with bare foot? 2) how look like the impact sound spectrums below 50 Hz (ISO) or below 100 Hz (ASTM)? 3) Can the tapping machine excite the low-frequency noise down to the range of wood floor natural frequency around 15 Hz? 3) Can the current measurement system measure the sound signals at such low frequency range reliably? 4) can the measured spectrums of impact sound signals reveal the low-frequency impact noise issues? 5) what are the special construction details of the wood floors contributing to the low-frequency impact noise problem? 6) what are the solutions for the problem? Our experiments answered the questions. The findings and solutions will be shared in the full-length paper.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".