STUDY ON VIBRATION PERFORMANCE AND STATIC DEFLECTION OF COLD-FORMED THIN-WALLED STEEL COMPOSITE FLOORS
Bibliographic record
Abstract
Full scale models of cold-formed thin-walled steel-profiled steel sheet floors and cold-formed thin-walled steel-gypsum based self-leveling mortar composite floors were subjected to vibration test under dynamic loading including walking and hammer impact, and to static test under 1 kN concentrated load. The effects on the fundamental frequency, damping ratio and mid-span vertical deflection of the composite floors were studied when floor panels were different and steel meshes were installed. The study shows that casting gypsum based self-leveling mortar on the profiled steel sheet could reduce the fundamental frequency, damping ratio and mid-span vertical deflection of composite floors. Nevertheless, it did not significantly increase the dynamic characteristics and decrease the vertical deflection of composite floor via installing steel mesh into gypsum based self-leveling mortar. ABAQUS finite element software was used to conduct modal analysis of the test models as well as variable parametric analysis based on the calibrated model. The research shows that the floor fundamental frequency could be improved and floor mid-span deflection could be reduced by increasing the web height, slab thickness of the floor joist and slab thickness of gypsum based self-leveling mortar, along with strengthening the floor end constraints. In theoretical calculation, the floor could be equivalent to a simply supported beam with uniform mass density and stiffness for predicting the fundamental frequency of cold-formed thin-walled steel composite floors. Additionally, the timber floor deflection calculation formula of Canada was recommended to predict the mid-span deflection of cold-formed thin-walled steel composite floors under 1 kN concentrated load.
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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 | 0.000 |
| 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".