Damping-Ductility Relationship for Performance Based Seismic Design of Shape Memory Alloy Reinforced Concrete Bridge Pier
Bibliographic record
Abstract
In performance based seismic design of structures, estimating the equivalent viscous damping (EVD) using a damping-ductility relationship is a major step. The seismic response of Shape Memory Alloy (SMA) reinforced concrete (RC) bridge pier is different from its traditional steel reinforced or post-tensioned counterparts. Due to its significantly different hysteretic response and material properties, it is necessary to estimate the EVD of SMA-RC pier and establish the damping-ductility relationship. An error in the estimation of equivalent viscous damping can lead to significant errors in the ductility demand of the designed pier. This study aims to develop expressions for equivalent viscous damping and damping-ductility relationship for SMA reinforced bridge pier when SMA rebars are used in the plastic hinge region of the bridge pier. New equivalent damping relations for five different bridge piers reinforced with five different types of SMAs have been developed. Nonlinear dynamic time history analyses were conducted on five different SMA-RC bridge piers using 100 ground motions. The accuracy of the new equivalent damping relation was assessed to ensure the applicability of the proposed relationships. Finally, a general expression was developed that can assist in estimating equivalent viscous damping for performance based seismic design of SMA reinforced bridge pier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".