Multi-variate seismic demand modelling using copulas: Application to non-ductile reinforced concrete frame in Victoria, Canada
Bibliographic record
Abstract
Joint probabilistic characteristics of key structural demand variables due to intense ground shaking are important for quantitative seismic loss estimation. Current damage–loss models require inputs of multiple seismic demand parameters, such as maximum/residual inter-storey drift ratio (ISDR) and peak floor acceleration (PFA). This study extends current seismic demand estimation methods based on incremental dynamic analysis (IDA) by characterising dependence among different engineering demand parameters (EDP) using copulas explicitly. The developed method is applied to a 4-storey non-ductile reinforced concrete (RC) frame in Victoria, British Columbia, Canada. The developed multi-variate seismic demand model is integrated with a storey-based damage–loss model to assess the economic consequences due to different earthquake loss generation modes (i.e. non-collapse repairs, collapse, and demolition). Results obtained from this study indicate that the effects of multi-variate seismic demand modelling on the expected seismic loss ratios are significant. The critical information is the limit state threshold for demolition. In addition, consideration of a realistic dependence structure of maximum and residual inter-storey drift ratios can be important for seismic loss estimation as well as for multi-criteria seismic performance evaluation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".