Estimation of Seismic Loss for Spatially Distributed Buildings
Bibliographic record
Abstract
A simulation‐based framework for assessing seismic risk of spatially distributed buildings is developed by taking the spatial correlation of seismic excitations into account. For each of seismic events compiled in a synthetic earthquake catalog, inelastic seismic demand on buildings that are approximated by bilinear single‐degree‐of‐freedom systems is compared with uncertain structural capacity to evaluate seismic damage severity. The proposed framework is employed to investigate the sensitivity of the estimated seismic risk of sets of buildings to the degree of spatially correlated and simultaneously occurring seismic excitations. In particular, four correlation levels—no correlation, full correlation, and partial correlation with/without intra‐event components—are considered. The assignment of the partial correlation is based on a recently developed spatial correlation model, and the sets of hypothetical buildings mimic existing building stocks in downtown Vancouver. The analysis results highlight that underestimation or overestimation of correlation of seismic demand could lead to very different probabilistic characteristics of aggregate seismic loss although its mean is unaltered. The sensitivity analysis results suggest that uncertainty in structural capacities as well as average local soil conditions is of relative importance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".