Towards a Computational Workflow for Studying the Effects of Climate Change on Wind Loads on High-Rise Buildings in Urban Areas
Bibliographic record
Abstract
Structures in the built environment that are serviceable under current climate conditions may experience problems in the coming decades as wind loads respond to anthropogenic climate change. Given the great uncertainty in the ability of current climate models to represent winds in the atmospheric boundary layer and the consequent uncertainty in the projection of future wind conditions, there is a need to formulate flexible adaptation strategies that mitigate the effects of climate change in urban regions. The present work thus proposes a multi-disciplinary workflow to investigate climate change-associated wind load effects on the built environment. As an application, projected, statistically downscaled surface wind information from global climate models is used to estimate future design wind speed for Toronto and Vancouver. CFD simulations are then performed on a building in downtown Toronto, under different projected wind scenarios. Wind loads on the building under different projected wind conditions are quantified and compared with the load associated with the National Building Code of Canada (NBCC) design wind speed. Using the proposed workflow, it was found that some climate models suggest reduced wind loads in the future (2071–2100) for buildings in Toronto, while others suggest the opposite. This cross-disciplinary workflow seeks to translate the range of projected effects of climate change into actionable knowledge useful for building design. This will deliver sustainability and resiliency-focused design, as well as retrofit recommendations for decision-makers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".