Which Ground Motion Intensity Measure Is Most Appropriate for Conditioning Demand Models for Bridge Portfolios?
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Bibliographic record
Abstract
Probabilistic seismic demand analyses are central to performance-based evaluation of structures and seismic risk assessments. The anticipated structural response and demand under earthquake loading is often characterized using a tool known as a probabilistic seismic demand model (PSDM). However, the degree of uncertainty in the model is dependent on the ground motion intensity measure (IM) used for conditioning the response (e.g. peak ground acceleration, spectral acceleration). Vulnerability assessments of general classes; or portfolios of structures, are becoming more essential because of their use in risk assessment packages such as HAZUS-MH, and hence the need for identification of optimal IMs increases. Appropriate intensity measures for general classes of bridges are evaluated as a part of this study, and the conditions under which various conclusions are valid. The influence of characteristics of the demand analysis on selecting an IM is assessed, such as the use of synthetic or recorded ground motions. The results are intended to offer guidance for appropriate intensity measure selection for probabilistic seismic demand models of bridge portfolios, which will considerably enhance future structural performance evaluations and regional risk assessments for transportation networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it