Application of Spatially Correlated and Coherent Records of Scenario Event to Estimate Seismic Loss of a Portfolio of Buildings
Bibliographic record
Abstract
This study extends the stochastic finite‐fault model (SFFM) to simulate spatially correlated and coherent records for a scenario seismic event and estimates the seismic loss of spatially distributed buildings using the simulated records. The extension incorporates the spatial coherency and the spatially correlated disturbance. The simulated records are used to evaluate nonlinear inelastic responses of buildings modeled as nonlinear single‐degree‐of‐freedom systems and to estimate their aggregate seismic loss. Use of the simulated records in such a manner is advantageous since it is applicable to buildings modeled as single‐ or multi‐degree‐of‐freedom systems with different hysteretic behaviors. The procedure is used to investigate the sensitivity of the seismic loss of a portfolio of hypothetical buildings in downtown Vancouver subjected to a scenario event. The results show that the probability distribution and the quantile of the seismic loss are influenced significantly by the degree of spatial correlation and by nonlinear inelastic behavior.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".