Fragility-Based Seismic Risk Assessment of Reinforced Concrete Bridge Columns
Why this work is in the frame
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Bibliographic record
Abstract
In earthquake-prone regions, predicting the impact of seismic events on highway bridges is crucial for post-earthquake effective emergency response and recovery planning. This paper presents a methodology for a simplified seismic risk assessment of bridges using fragility curves that integrates updated ductility ratios of reinforced concrete bridge columns from literature based on experimental results on cyclic tests of reinforced concrete circular columns. The methodology considers two damage states (cover spalling and bar buckling) for bridge columns with seismic and non-seismic design considerations and then estimates displacement thresholds for each damage state. The Damage Margin Ratio (DMR) is introduced as an index defined by the ratio of the median Peak Ground Acceleration (PGA) for a specific damage state to the PGA that corresponds to the target seismic hazard probability of exceedance in 50 years that is typically defined in bridge design and evaluation codes and standards. The DMR is then compared to a user-specified Threshold Damage Margin Ratio (TDMR) to evaluate the level of risk at a specific threshold probability of exceedance of the damage state (5% and 10%). Comparative assessment is conducted for the relative seismic risk and performance of non-seismic and seismic bridges corresponding to the seismic hazard values at 10% and 2% probability of exceedance in 50 years for 7 urban centers in the province of Quebec as a case study demonstration of the methodology. The proposed methodology offers a rapid tool for screening and prioritizing bridges for detailed seismic evaluation.
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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