A new smooth hysteretic model for ductile flexural‐dominated reinforced concrete bridge columns
Bibliographic record
Abstract
Summary A new smooth hysteretic model is proposed for ductile, flexural‐dominated reinforced concrete bridge columns. Four columns designed per modern seismic codes were tested using monotonically increasing and variable‐amplitude cyclic loading protocols and ground motion loading to develop the model. Based on the test results, hysteretic rules for damage accumulation and path dependence of reloading were constructed. For damage accumulation, unloading stiffness degradation is correlated with the maximum displacement and hysteretic energy dissipation, while reloading stiffness degradation is set equal to the unloading stiffness degradation. Pinching severity is related to the residual displacement in the direction opposite to the loading direction. Strength deterioration is correlated with the damage index and does not occur until the damage index reaches a threshold, after which the deterioration is proportional to the increase of the damage index. For path dependence of reloading, reloading paths are classified into primary paths and associate paths. The primary paths are those that start from a residual displacement that is equal to or larger than the previous maximum one. The associate paths are those that do not belong to primary paths and tend to be directed towards certain points. Reloading without load reversal is assumed to be linear. Comparison with the results of pseudo‐dynamic tests using three consecutive ground motions showed that the proposed model closely matched the test results. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".