Performance-based wind design of tall buildings : concepts, frameworks, and applications
Bibliographic record
Abstract
The rapid growth of the urban population and the associated environmental concerns are central challenges of the 21st century. In response, recent urban design strategies in North America, Asia, and Europe are exploring the use of sustainable construction materials and performance-based design approaches for tall buildings. Timber is a sustainable and renewable construction material. The use of engineered wood products within mass-timber buildings makes them lighter and more flexible than concrete or steel buildings. These characteristics can potentially result in excessive dynamic oscillations when excited by strong winds and thus limit the height that they can reach. Mass-timber buildings exceeding 12 stories are an exception in the 2020 Canadian building codes; hence they can only be realized using performance-based design approaches. Furthermore, the current wind design practice for tall buildings considers the first significant yielding point as the ultimate limit state, making tall buildings costly due to an excessive design safety margin. Hence, to overcome these limitations, the subject of this dissertation is to develop and apply new performance-based wind design (PBWD) frameworks for tall buildings. Initially, this dissertation develops two unified PBWD frameworks for the design of tall buildings by adapting and revisiting the Alan G. Davenport Wind Loading Chain. Thereafter, the issue of damage accumulation in PBWD is studied in two steps. The first step performs a parametric study through nonlinear response history analyses (NRHA) of bilinear and self-centering SDOF systems under long-duration along-wind loads. The results from this step show the capability of self-centering systems in controlling the possible wind-induced damage accumulation. The second step demonstrates the benefit of PBWD in terms of economics and safety through structural reliability analysis. Subsequently, the application of the PBWD frameworks is presented in four phases. The first three phases focus on the performance-based serviceability design of tall mass-timber buildings. The fourth phase presents two examples illustrating the PBWD of 40-story tall steel buildings with self-centering braces. NRHA are conducted to assess the studied buildings' performance beyond the first significant yield point. Overall, the results show the possibility of achieving economic and enhanced structural performance through PBWD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".