Closed-Form Fragility Estimates, Parameter Sensitivity, and Bayesian Updating for RC Columns
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Bibliographic record
Abstract
Reinforced concrete (RC) columns are the most critical components in bridges under seismic excitation. In this paper, a simple closed-form formulation to estimate the fragility of RC columns is developed. The formulation is used to estimate the conditional probability of failure of an example column for given shear and deformation demands. The estimated fragilities are as accurate as more sophisticated estimates (i.e., predictive fragilities) and do not require any reliability software. A sensitivity analysis is carried out to identify to which parameter(s) the reliability of the example column is most sensitive. The closed-form formulation uses probabilistic capacity models. A Bayesian procedure is presented to update existing probabilistic models with new data. The model updating process can incorporate different types of information, including laboratory test data, field observations, and subjective engineering judgment, as they become available.
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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 | 0.001 | 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