Seismic loss and resilience assessment of a steel building retrofitted with self‐centering buckling‐restrained braces
Bibliographic record
Abstract
Self‐centering buckling‐restrained braces (SCBRBs) can be an effective seismic retrofitting measure for older steel moment frame buildings designed based on outdated design provisions. SCBRBs can considerably improve the strength and ductility capacity and are particularly efficient at mitigating residual deformations, a critical parameter often adopted as a demolition metric. However, this retrofit strategy can simultaneously lead to an increase in seismic demands on floors because of increased lateral stiffness which increases the potential for damage to non‐structural elements (NSEs). Damage to NSEs can render buildings unoccupiable for an extended period even if the structural damage is minor. In this study, the seismic resilience of a case study moment‐resisting steel frame building is compared to one retrofitted with SCBRBs. In particular, the effect of the SCBRB retrofit on NSEs is examined and their contribution to the total expected economic losses is quantified. Furthermore, various scenarios are evaluated in which NSEs are also retrofitted to illustrate their importance to functional recovery. Analysis results reveal that damage to the ceiling system and partition walls, which can be amplified as a result of added lateral stiffness from SCBRBs, can significantly delay the recovery process. Moreover, recovery delays associated with mechanical components can be reduced by enhancing the seismic behavior of integral items such as elevators. These results demonstrate how retrofit strategies that alter a building’s seismic response such as SCBRBs can have unintended consequences on NSEs and adversely impact seismic resilience.
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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 | 0.001 |
| 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".