Human-induced vibration of cold-formed steel floor systems: Parametric studies
Bibliographic record
Abstract
Designing a lightweight floor to prevent annoying vibrations induced by human activities is still a challenge because numerous factors need to be considered. In consequence, there is still a lack of reliable models and adequate design guidelines pertinent to the vibration serviceability of cold-formed steel floor systems. To facilitate understanding the fundamental concepts of lightweight floor vibrations for serviceability design, a newly proposed damped plate-oscillator model was adopted in this research to predict dynamic responses of cold-formed steel floors induced by human walking. Three loading methods were developed based on this model. By using these loading methods, comprehensive parametric studies involving step frequencies, mass ratios, damping ratios, walking paths and end-support restraints were conducted. The present analytical studies show that the influence of moving or stationary occupants depends on the mass ratio of occupants to the floor and its significance could be negligible for small mass ratios. In addition, the boundary restraints at floor end-supports may not always reduce floor responses. On the contrary, relatively larger responses could be excited by human walking for the restrained floors having the fundamental frequency close to the multiple of the footstep frequency.
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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 | 0.000 | 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) | 0.001 | 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".