Probabilistic Postearthquake Vertical Load-Carrying Capacity Loss Model and Rapid Functionality Assessment for Reinforced Concrete Circular Bridge Columns
Bibliographic record
Abstract
The rapid and accurate postearthquake traffic capacity or functionality loss assessment for highway bridges after a strong earthquake is crucial to the decision-making for postearthquake emergency rescue and recovery, as well as seismic resilience analysis. The postearthquake traffic capacity of a simply supported highway girder bridge designed based on the capacity design philosophy is generally dominated by the loss of postearthquake vertical load-carrying capacity of the damaged bridge column, which, however, cannot be rapidly and quantitatively evaluated by previous studies. This study develops a postearthquake vertical load-carrying capacity loss model for flexure-dominated circular RC bridge columns using multiple linear regression, which is a function of column-related structural parameters and a selected damage indicator (i.e., residual drift ratio). The database of the postearthquake vertical load-carrying capacity loss for the damaged but before collapsed RC columns is generated through numerical simulations using a loading scheme consisting of nonlinear time-history analysis followed by pushdown analysis. In order to generate a sufficient database of the vertical capacity loss of RC columns, an incremental dynamic analysis (IDA) approach is adopted to produce different damage levels on the column. The significance of the loss regression model and the significance of each corresponding regression coefficient are checked by statistical tests. In addition, the generalization ability of the loss model is also tested by 10-fold cross-validation. After that, a probabilistic postearthquake vertical load-carrying capacity loss model is developed in this study. Based on this probabilistic model, a traffic capacity fragility curve conditioned on the residual drift ratio of a given column is proposed in this study for the first time to assess the remaining functionality of a given RC column. This proposed traffic capacity fragility curve is further validated by two example columns with and without considering the uncertainty, respectively. The traffic fragility curve can be quickly generated for the target circular RC bridge column and facilitate the postearthquake decision-making for the damaged but before-collapsed column, thus forwarding to the introduced rapid postearthquake assessment for simply supported highway girder bridges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".