Seismic reliability-based assessment and design optimization of shape memory alloy bars in concrete bridge piers
Bibliographic record
Abstract
Utilizing shape memory alloy (SMA) bars to reinforce the plastic hinge of concrete bridge piers is regarded as an effective alternative to enhance the seismic performance and resilience of highway bridges. However, how to evaluate the seismic reliability of such a performance enhancement scheme and optimize the usage of SMA to reach a better economic performance is still lacking in the field of performance-based earthquake engineering. To this end, this study provides a reliability-based methodology to evaluate the seismic performance of bridge piers with a hybrid reinforcement consisting of SMA and regular steel bars and to optimize the usage of SMA in the plastic hinge of the bridge pier. A reliability-based assessment and design optimization methodology is introduced first in this study. The finite element (FE) model of a benchmark bridge pier with five different replacement ratios of SMA is then modeled considering the uncertainty of material-related parameters and the bond-slip effect of longitudinal bars. A shake table test on an SMA-reinforced concrete bridge column available in the literature is selected to validate the numerical model. Based on the seismic hazard curve of the bridge site, a set of site-specific ground motions is selected through the uniform risk spectra. The peak drift ratio and residual drift ratio are separately adopted as the damage indicator, which is linked to the damage state of the bridge pier. The seismic fragility and reliability index associated with each damage state is presented as a surface varying with the ground motion intensity measure (IM) level and the SMA replacement ratio, according to the incremental dynamic analysis results. Finally, the reliability-based design optimization approach is implemented in this study to optimize the usage of SMA. The results indicate that the SMA can significantly reduce the conditional damage probability and failure probability when using the residual drift ratio as a damage indicator, resulting in an improvement in seismic reliability. Optimizing the usage of the SMA bar in the plastic hinge of bridge piers can balance the bridge’s construction cost and structural performance. • A seismic reliability assessment of the concrete bridge pier with different proportions of SMA reinforcements is performed. • Seismic reliability-based design optimization of SMA bars in concrete bridge piers is proposed and implemented in this study. • The initial construction cost of a regular RC pier, pre-optimized and post-optimized SMA piers is compared. • The post-optimized SMA pier not only meets the prescribed performance but also reduces the initial constriction cost.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".