VIBRATION SERVICEABILITY PERFORMANCE OF MASS TIMBER FLOORS UNDER VARIOUS SUPPORT CONDITIONS
Bibliographic record
Abstract
Mass timber floors are prone to human-induced vibration due to their light weight.Vibration serviceability limit design often governs the maximum allowable span of mass timber floors.The current design methods including the vibration-controlled span equation in CSA O86-19 and the design method in EC5 usually assume the mass timber floors are simply supported on rigid walls, which can't be directly applied to floors being supported by beams.In this study, the vibration performance of mass timber floors including nailed laminated timber, dowel laminated timber, and crosslaminated timber floor panels was investigated experimentally.The effect of various support conditions on the dynamic properties of mass timber floors was studied through modal testing, and the vibration acceptability of these floors under normal human walking was assessed by subjective evaluations.The test results indicated that the stiffness of the support significantly impacts the dynamic properties and vibration performance of the entire floor slab.The performance criterion specified in CSA O86 demonstrated potential for accurately predicting the vibration performance of beam-supported mass timber floors.However, both the vibration-controlled span equation and the beam stiffness equation were found to be insufficient for designing such floors.The vibration response-based design methods that utilize the ISO 10137 baseline curve showed inconsistencies across all groups.Dunkerley's system frequency prediction equations yielded overestimated results, while Kollar's method exhibited an average error within 5%, demonstrating promising potential for practical use.Further research is required to develop a reliable design approach for beam-supported mass timber floors.
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 | 0.001 |
| 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) | 0.002 | 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".