Quantifying the effect of end support restraints on vibration serviceability of mass timber floor systems: Analysis and design
Bibliographic record
Abstract
Current design methods for assessing vibration serviceability in mass timber floors typically assume simply supported conditions at their ends, overlooking the influence of support restraints provided by fastenings and top loads (i.e., loads transmitted from upper storeys). This study addresses this limitation by quantifying end support restraints for practical design applications through a combination of testing and analytical modelling, presented in companion papers. As the second part of this investigation, this paper explores the clamping mechanisms of fastenings and top loads, develops analytical models for support restraints, and derives semi-empirical formulas to incorporate these effects into design practices. The study begins by analysing the clamping mechanism of self-tapping screws, converting their restraining effects into equivalent top loads. This forms the basis for a unified model that captures the combined restraining effects of both top loads and self-tapping screws. To represent restraints caused by the top loads, the concept of effective lever arms was introduced. Semi-empirical formulas were then developed based on extensive test results and validated through various test programs and numerical modelling studies, ensuring broad applicability to various mass timber floor systems. Finally, the proposed design approach was demonstrated through a case study on the vibration serviceability of cross laminated timber floors, showcasing its effectiveness and practical relevance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".