Full-scale monitoring of a tall, slender building with viscoelastic coupling dampers
Bibliographic record
Abstract
Tall, slender buildings are sensitive to dynamic vibrations caused by wind, and occupant motion perception may be a critical design parameter. A method to control dynamic vibrations is to increase the damping characteristics of the structure, which may be accomplished using a supplemental damping system. A novel configuration for implementing solid viscoelastic damping in tall buildings, the Viscoelastic Coupling Damper (VCD), was implemented in a tall, slender building in downtown Toronto. The VCDs were designed to provide supplemental damping in the first and third modes of vibration to improve occupant comfort. This building was the subject of a year-long monitoring program where output-only system identification algorithms were applied to track the evolution of the dynamic properties through the construction of the building. Larger amplitude wind events occurred during the program, including an event with large insured losses in Ontario, allowing for the tracking of amplitude-dependent properties. The effect of the VCD system was assessed, and calibrated finite-element models were constructed with reference to the experimental results. It was found that amplitude-dependent trends were observed for the natural frequency, damping ratio, and the global movement of the building relative to the shear deformation in the VCD. It was also found that the VCDs provided the targeted supplemental damping of 0.9 % in the first and third modes, with a total damping in the building exceeding 2.5 % at larger amplitudes observed during a large wind loading event. The measured natural frequency of the first three modes of vibration and the local damper response matched the calibrated finite element models well.
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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".