Accuracy and uncertainty of predicted maximum and residual displacements of RC bridge columns under earthquake excitations
Bibliographic record
Abstract
Fundamental to the performance-based seismic design is the accuracy of predicted responses under earthquake excitations. This study evaluates the accuracy and uncertainty of predicted maximum and residual displacements of concrete bridge columns subjected to different types of ground motions. A series of nonlinear time-history analyses considering a wide range of element formulations and model parameter combinations was performed to reproduce measured responses from previous shake table tests. Variations in element formulation had little influence on the accuracy and uncertainty of predicted maximum displacements. The gradient inelastic force-based element, however, predicted bar tensile strain profiles across the plastic hinge with higher resolution, but at a higher computational cost than displacement-based and beam with hinges elements. Models with tangent stiffness-based Rayleigh damping produced the most accurate predictions of the maximum drift ratios with an RMSE of 0.011, indicating that the maximum displacement (or drift ratio) can be predicted with reasonable accuracy. Residual displacements, on the other hand, were predicted with unreasonable levels of error and uncertainty. A data-driven model was thus proposed to correct predicted residual displacement. Following correction, the RMSE of the predicted residual drift ratios was reduced by 43 % to 0.006. • The effect of modeling parameters on predicted maximum and residual drifts of RC bridge columns is evaluated. • The most effective modeling combination is identified. • A correction factor for predicted residual drifts is proposed. • Uncertainty of predicted maximum and residual drifts is quantified.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".