Modal testing and FE model correlation and updating of a long-span prestressed concrete floor
Bibliographic record
Abstract
There is a lack of information on as-built vibration properties and performance of slender prestressed post-tensioned (PT) concrete floors in buildings. To improve this situation, this paper describes modal testing, finite element (FE) modelling, and model correlation and updating of one such prototype floor. Advanced testing, correlation and updating technologies demonstrated in this paper were successfully transferred from mechanical and aerospace engineering disciplines where they are nowadays used as a standard design tool. Two structural configurations of the full-scale floor were examined: the bare floor in the unclad building, and the same bare floor but in the clad building with services attached underneath. Modal testing showed a considerable increase in stiffness in the latter case. The FE model correlation and updating exercise quantified this contribution and also demonstrated that bending of in situ cast concrete columns supporting the in situ cast concrete floor contributed significantly to the floor bending stiffness and, therefore, should not be modelled as pin-supports. Finally, wide and shallow band-beams, which often feature in PT floors, were shown to have considerable lateral stiffness. This feature has the potential to improve the floor's vibration performance, and was successfully modelled using orthotropic shell elements.
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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 | 0.002 |
| 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 | 0.001 | 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".