Optimal Intensity Measure Selection and Fragility Curve Development for Highway Bridges Under Long-Duration Ground Motions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper investigates optimal intensity measures (IMs), a crucial parameter in developing probabilistic seismic demand models for accurately predicting structural demands on bridges subjected to long-duration (LD) ground motions. By analyzing various IMs and structural demands, the study identifies the most effective IMs for different durations and site characteristics across five regions: Chile, New Zealand, Taiwan, Turkey, and the United States. The results indicate that the optimal IMs are PGV for Chile, Sa(T1) and Sa1.0 for New Zealand, PGVand Ia for Taiwan, SaT1 and Sa1.0 for Turkey, and PGV and Sa1.0 for the United States. Finally, the study utilizes these optimal IMs to develop fragility curves, providing a comprehensive assessment of bridge vulnerability under LD seismic events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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