Effects of full-height nonstructural partitions on modal properties of two nominally identical building floors
Bibliographic record
Abstract
This paper presents a combined experimental and numerical investigation of the modal properties of two full-scale and nominally identical steel–concrete composite floors. The floors were one above the other in the same fully operational multi-storey building. Both floors accommodated open-plan as well as partitioned offices. Multi-input-multi-output (MIMO) modal testing was employed to measure as-built modal properties of both floors. It was found that the two nominally identical floors had different modal characteristics, likely due to the different arrangement of partitions in the floor. It was also found that the measured modes on both floor levels experienced a considerable level of complexity, likely to be caused by nonproportional damping. Finite element (FE) models were developed in ANSYS for both floors using best engineering judgement and their features and properties were then tuned to match the measured counterparts. The tuning was done manually by trial-and-error and then automatically using sensitivity-based FE model updating procedure implemented in the FEMtools software. It was found that the initial and geometrically very detailed FE models, which did not feature any nonstructural components, underestimated the measured natural frequencies by up to a considerable 20%–25%, depending on the floor level. When full-height plasterboard and glass partitions were explicitly modelled as vertical springs connected to the floor and grounded at the other end, the correlation between the experimental and FE results improved considerably.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".