Seismic response sensitivity and optimal design of an isolated multi-span continuous highway bridge with self-centering SMA RC bridge piers and superelastic SMA restrainers
Bibliographic record
Abstract
The use of superelastic shape memory alloy (SMA) in reinforced concrete (RC) pier and restrainers between girder and pier/abutment has been proposed in an earlier study to improve the seismic performance of highway bridges. This paper aims at identifying the significant factors that affect the seismic response of such a novel bridge system. A sequential fractional factorial design method is performed to statistically evaluate the effects of six design factors (three geometry-related and three material-related) as well as their interactions. Additionally, a multi-criteria optimization technique is implemented to determine the most efficient combination of the design parameters for SMA RC piers and SMA restrainers. Results demonstrate that the geometry-related factors and their interactions have large effects (with a contribution greater than 91%) on the relative displacement between the girder and pier. The target residual drift of the pier, design target displacement of the restrainer, and their interaction are the three most significant factors (with contributions approximately 30%–68%) affecting the base shear of the pier. The residual drift of the pier is sensitive to the design target displacement of the restrainer, its interaction with the forward transformation stress of SMA, and material-related factors with regard to the energy dissipation of SMA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".