Performance Based Seismic Design of Shape Memory Alloy Reinforced Concrete Bridge Pier
Bibliographic record
Abstract
<p>Recent advancements in numerical analysis and computational power have pushed the current bridge design specifications towards a more descriptive performance-based seismic design (PBSD) approach as compared to the conventional force-based method. Shape memory alloy (SMA), with its distinct superelasticity, shape memory effect and hysteretic damping, is a promising material for the application in bridge piers to attain the objectives of PBSD. Despite few experimental testing to demonstrate the efficacy of SMA as reinforcement in bridge pier, there has been a lack of comprehensive guidance for potential designers of SMA-RC bridge piers. This paper proposes a performance-based design methodology for SMA-RC bridge piers, which consists of defining the performance objectives, developing performance based damage states and formulating a performance based design guideline considering maximum and residual drift. The procedure anticipates the allowable residual drift based on target performance level, calculates the maximum allowable drift, and ensures that those deformation demands remain below the allowable residual and maximum drift. Guidelines to determine the target drift and effective damping properties for SMA-RC bridge piers are also provided. The proposed procedure and guidelines are used in a trial application to design a SMA-RC bridge pier and analysed using a suite of selected earthquake records. The nonlinear analyses showed that the designed pier behave according to design expectations and provided very promising results in terms of the effectiveness and applicability of the proposed design method.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".