Seismic Collapse Risk Assessment of Code-Conforming RC Moment Resisting Frame Buildings Designed With 2014 Canadian Standard Association Standard A23.3
Bibliographic record
Abstract
This paper presents collapse risk assessment of code-conforming reinforced concrete moment resisting frame (RC MRF) buildings located in Vancouver, Canada. This assessment investigates the three- and six-storey of regular RC MRF building systems, with and without unreinforced masonry infill wall, representing low- to mid-rise structures. These buildings are designed according to the current National Building Code of Canada and detailed based on the 2014 Canadian Standards Association A23.3 standard provision for high seismic regions. Two different ductility classes of seismic building design, namely ductile and moderately ductile, are considered to identify the capability, equality and/or difference of the seismic performance of these designed buildings. Nonlinear dynamic analysis is applied in the performance-based seismic assessment procedures to assess the collapse response of structural for the set of 50 pair ground motion records. Next, the seismic fragility curves are developed through incremental dynamic analysis. Finally, mean annual frequency of collapse is calculated through combination of fragility curve and hazard curve. The results indicate that the bare RC buildings are vulnerable to earthquake-induced collapse when the number of the story increased. The presence of the URM infill walls significantly influence the collapse behavior of the frame structure. Compared to moderately ductile MRF buildings, ductile MRF buildings show a better collapse performance, is strongly influenced by the capacity of the building system.
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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 | 0.000 |
| 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) | 0.000 | 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".