Spectral shape metrics and structural collapse potential
Bibliographic record
Abstract
Summary This paper examines various parameters that provide a measure of spectral shape and studies how they relate to the potential of ground motion records to cause the collapse of a given structure. It is shown that when measuring the ground motion intensity by the spectral acceleration at the first‐mode period of the structure, Sa(T1), records causing collapse at low ground motion intensities typically have significantly different spectral shapes than those that do not cause collapse until much higher ground motion intensities. A spectral shape typical of damaging records is identified, and a metric for quantifying the spectral shape of a record called SaRatio is proposed and evaluated. SaRatio is defined as the ratio between Sa(T1) and the average spectral value over a period range. The ability of SaRatio to predict the collapse intensity, i.e. the minimum intensity at which a given ground motion causes the collapse of a given structure, is compared to other recently proposed spectral shape metrics including epsilon (ε), eta (η) and Np. The results demonstrate that SaRatio is typically a much better predictor of collapse intensity than other spectral shape metrics. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".