Seismic collapse risk of RC-timber hybrid building with consideration of NBCC 2020 seismic hazard model
Bibliographic record
Abstract
Abstract The latest seismic hazard model described in the 2020 National Building Code of Canada (NBCC) marks a comprehensive improvement over its predecessor (NBCC 2015). For different regions in Canada, this will have an impact on the design of new buildings and performance assessment of existing ones. In the present study, this impact is quantified for a recently developed hybrid building system of cross-laminated timber (CLT) and reinforced concrete (RC) with steel slit dampers. The six-story CLT-RC dual frame, designed using the direct displacement-based method, is located in Vancouver, Canada. Along with a very high seismicity, southwestern British Columbia is characterized by complex seismotectonics consisting of subduction, shallow crustal, and in-slab faulting mechanisms. With the NBCC 2015 and 2020 models, a hazard-consistent set of 40 pairs of ground motion records is selected from the PEER and KiK-net databases, and used to estimate the building’s seismic risk performance. The collapse capacity of the building reveals the need for the use of site-specific ground motion selection for sites with a complex seismicity. The results also indicate that the combination of CLT and steel slit dampers enhances the seismic performance of the hybrid system. The probability of collapse at a hazard level corresponding to a 2475 year return period event is estimated to be 2.7%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".