Collapse Risk of Tall Nonductile Reinforced Concrete Shear Wall Buildings
Bibliographic record
Abstract
Existing buildings, particularly those predating modern building codes, pose significant seismic risk in regions of high seismicity worldwide. In the City of Vancouver, British Columbia, Canada, reinforced concrete shear wall (RCSW) buildings are prevalent in the construction of tall buildings. Many of these buildings were constructed before the introduction of ductility requirements in the Canadian concrete standard during the mid-1980s, and they predominantly serve as residences for renters, seniors, and low-income populations. This study quantifies the collapse risk of these tall nonductile RCSW buildings to understand their seismic vulnerability. Leveraging a comprehensive database of pre-1980 RCSW buildings, a framework is proposed to generate representative archetypes using a random forest regression model. An automated workflow is developed to facilitate nonlinear structural analyses of these buildings, and a sample of 25 archetypes of varying heights, i.e., 10–30 stories, is selected to evaluate their seismic performance. The results indicate a high risk of collapse, ranging from 9% to 29% in 50 years, significantly higher than the collapse risk target of 1% in 50 years in US standards. The results also indicate that collapse risk can be significantly underestimated when (1) taxonomy-level fragility functions are employed to characterize the performance of this unique typology of buildings, and (2) when the ground motion amplification effects of the Georgia sedimentary basin below Metro Vancouver are neglected.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".