Improved intensity measures for probabilistic seismic demand analysis. Part 2: application of the improved intensity measures
Bibliographic record
Abstract
This is the second of two companion papers on improved intensity measures of strong seismic ground motions for use in probabilistic seismic demand analysis of reinforced concrete frame buildings. The first paper discusses the development of improved intensity measures. This paper describes the application of the developed intensity measures in probabilistic seismic demand analysis. The application is illustrated on the three reinforced concrete frame buildings (4, 10, and 16-storey high) that were used in the first paper. This involved computations of the seismic responses of the structures and the seismic hazard using the improved intensity measures. The response and the hazard results were then combined by means of probabilistic seismic demand analysis to determine the mean annual frequencies of exceeding specified response levels due to future earthquakes (i.e., the probabilistic seismic demands). For the purpose of comparison, probabilistic seismic demand analyses were also conducted by employing the spectral acceleration at the fundamental structural periods (Sa(T1)) as an intensity measure, which is currently the most used in practice. It was found that the use of the improved intensity measures results in significantly lower seismic demands relative to those corresponding to the intensity measure represented by Sa(T1), especially for long period structures.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".