Deaggregation of Collapse Risk
Bibliographic record
Abstract
Avoidance of collapse is one of the most important objectives when designing structures in seismic regions. This study focuses on collapse risk quantification and, in particular, deaggregation of the mean annual frequency of collapse. Similar to the deaggregation used in probabilistic seismic hazard analysis, which provides information about the magnitudes, distances, and epsilon values that primarily contribute to the seismic hazard, deaggregation of the mean annual frequency of collapse is a powerful tool that identifies the ground motion intensities that primarily contribute to the collapse risk of a given structure. A detailed description of how to conduct this deaggregation is presented, and the concept is illustrated using a 4-story steel special moment frame building designed according to the 2003 International Building Code. The collapse risk of this structure is evaluated for sites in the western, central, and eastern United States. Despite significant differences in the seismic hazards at each site, it is shown that intensities corresponding to the lower half of the collapse fragility curve dominate the collapse risk at all sites. It is concluded that emphasis should be placed on the lower half of the collapse fragility curve as opposed to focusing on the median collapse intensity as is currently done.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".