Review of Vibration Assessment Methods for Steel-Timber Composite Floors
Bibliographic record
Abstract
Human comfort is recognized as an essential serviceability requirement for timber floors. Although several standards and design criteria are available for designing steel and concrete floors, there is no consensus among researchers on the applicability of such design methods to timber composite floors. Adding steel to timber floors is intended to create long spans, however, vibration is still a major challenge in achieving longer spans. To highlight the extent of this issue, a comprehensive search in the literature was conducted. The most common vibration criteria that may be used to assess the performance of steel-timber composite floors under human-induced vibrations were reviewed. For lightweight composite floors, the 1 kN deflection limit was found to be the most suitable vibration limit based on a wide range of subjective evaluation studies. For composite floors comprising steel and heavier timber subfloors, the relevance of 1 kN deflection criterion and other criteria suggested in the literature are questionable due to the lack of subjective evaluation studies. In the advent of advanced computing and data analysis, conducting detailed numerical analysis validated by accurate on-site measurements is recommended. Special attentions should be given to accurate estimation of connection stiffness and damping ratio according to the findings of this study.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".