Seismic performance assessment of a multispan continuous isolated highway bridge with superelastic shape memory alloy reinforced piers and restraining devices
Bibliographic record
Abstract
Abstract The objective of this study is to analytically determine the effectiveness of a novel bridge system with superelastic (SE) shape memory alloy (SMA) reinforced concrete piers. The bridge is also equipped with SE SMA cable restrainers to prevent the bridge spans from a large displacement that can potentially cause span unseating. In the concrete bridge piers, the conventional steel reinforcements in the plastic hinge regions are replaced with SE SMA rebar to avoid large plastic deformation and improve its self‐centering capacity. A typical three‐span continuous highway bridge is modeled with SMA‐reinforced piers and SMA restrainers. Numerical simulations of the bridge are conducted under destructive near‐fault ground motions. The seismic responses and fragility curves of the novel bridge (Bridge IV) are assessed and compared with the reference bridge (Bridge I), the bridge with only SMA‐reinforced piers (Bridge II), and the bridge with only SMA restrainers (Bridge III). The results revealed that the SMA‐reinforced pier can successfully reduce the residual deformation and damage probability of the bridge; however, the bridge with only SMA‐reinforced piers is less efficient in preventing a large displacement. The use of SMA restrainers can efficiently limit the displacement of the bridge spans but increase the damage probability of the bridge piers. The proposed novel bridge having SMA‐reinforced piers equipped with SMA restrainers (Bridge IV) is more efficient than the bridge with only SMA‐reinforced piers (Bridge II)) or the bridge with only SMA restrainers (Bridge III).
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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) | 0.000 | 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) | 0.001 | 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".