Utilizing a new self-centering hysteresis model to assess the seismic vulnerability of a long-span cable-stayed bridge equipped with SMA wire-based roller bearings
Bibliographic record
Abstract
Abstract A novel shape memory alloy wires-based smart roller bearing (SMA-RBs) has been developed and its cyclic behavior under reverse cyclic loadings has been experimentally investigated. However, its efficacy and performance in enhancing the seismic performance of bridge structures have not been well understood and proven. A new self-centering hysteresis model for SMA-RBs has been proposed to properly simulate their hysteretic behavior, which has been experimentally validated through a pseudo-static test. A methodology is proposed to determine the four damage states of SMA-RB (i.e. slight, moderate, extensive, and collapse) considering the contribution of SMA wires. The smart SMA-RBs are utilized for a cable-stayed bridge in China. The vulnerability of two reference bridges, i.e. the floating system (FS) and rigid system (RS), and one isolated bridge equipped with SMA-RBs (SMA-RBS) are compared at component and system levels. The applicability of three commonly used intensity measures (IMs), i.e. PGA, PGV, and Sa(T 1 ), are evaluated and PGV turns out to be the optimal IM for long-span cable-stayed bridge systems. Results show that incorporating SMA wires in roller bearings can decrease the failure probabilities of the bearing. The piers and towers with SMA-RBs lead to lower seismic fragility over the towers and piers in the reference bridges. The RS is the most vulnerable bridge whereas the SMA-RBS is the least vulnerable bridge among the four bridges. The SMA-RBS experience a much lower collapse damage probability compared to RS ad FS.
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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 | 0.000 |
| 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".