Dynamic performance of tall wood buildings with fluid viscous dampers
Bibliographic record
Abstract
,As lightweight and slender tall timber designs gain popularity, seismic loads and wind-induced vibrations are becoming a prominent concern in modern structural engineering. The reduced mass and stiffness of these structures render them flexible and, consequently, more susceptible to dynamic oscillations, which can affect both life safety and occupant comfort. Moreover, knowledge of damping characteristics in tall timber buildings is limited because of the relatively small number of completed projects. Nonetheless, as building height increases, a corresponding decrease in damping values becomes evident, rendering tall structures more susceptible to lateral drift and occupant discomfort during seismic and wind events. In response, the British Columbia Building Code has recently limited timber buildings to 18 stories, underscoring the need for advanced seismic and wind mitigation strategies in such designs. This thesis addresses these challenges by developing numerical models of three 18-story timber buildings and subjecting them to dynamic wind analysis and nonlinear time history seismic analysis to capture their complex oscillatory behaviour. Fluid viscous dampers (FVDs) were strategically integrated into each model to align overall performance with the National Building Code of Canada criteria. The findings indicate that using FVDs reduces lateral drift, particularly in regions prone to seismic activity, and significantly enhances occupant comfort under wind-induced vibrations. Moreover, comparative assessments of multiple damper configurations illuminate how these devices can effectively balance wind and seismic demands, offering more profound insight into optimizing tall timber structures. In conclusion, this work confirms the viability of modern tall timber buildings as a resilient, ecofriendly solution while providing practical guidelines for damper integration to safeguard both structural integrity and occupant well-being in the face of multifaceted dynamic loads.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".