Vibration performance of CLT and CLT-concrete composite floors supported by glulam beams under human activity in mass timber office buildings
Bibliographic record
Abstract
Timber floors are susceptible to vibration due to their low mass and bending stiffness. Utilizing mass timber products in long-span scenarios, such as in office buildings, makes vibration an important design driver for structural engineers. To gain insight on the vibration performance of mass timber and timber-composite floors in real mass timber buildings, a comprehensive testing campaign was conducted on two mass timber office buildings. The measured data were compared and discussed according to various standards and design guides at the end. Although some discrepancies between estimated parameters and measurements were noted, the floors reasonably meet their desired performance objectives for office buildings according to existing standards. The results presented in this paper not only demonstrate the effect of human weight and walking path on the floor’s response, but also provide important data on mass timber floor system performance in furnished buildings, which is not otherwise available in the literature. • Experiments on long-span CLT floors with and without concrete layers. • Several walking tests conducted at different pace rates in selected bays. • Satisfactory performance of CLT floors in two office buildings. • Detailed assessment of measured data according to codes and standards. • Higher damping ratios in mass timber office buildings compared to those in laboratory.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".