Performance-Based Retrofitting of Aging RC Frames Using Innovative Knee-Braced Buckling-Restrained Braces for Enhanced Seismic Resilience
Bibliographic record
Abstract
The performance of reinforced concrete (RC) structures is a critical area of research, given the increasing need for resilient infrastructure in seismically active regions. Deficient RC frames, commonly found in aging buildings, exhibit limited ductility and energy dissipation capacity, rendering them vulnerable to seismic events. Currently, the literature lacks a study regarding the performance of knee-braced buckling-restrained braces (KBRBs) as a strengthening technique for deficient RC frames. Unlike traditional bracing systems, KBRBs offer enhanced energy dissipation and deformation capacity, eliminating the drawbacks associated with brace buckling. The motivation for this study stems from the need to develop innovative retrofit strategies that can enhance the seismic resilience of deficient reinforced-concrete frames while overcoming their inherent shortcomings. This study examines the seismic behavior of deficient reinforced-concrete frames strengthened with knee-braced, buckling-restrained brace systems using numerical analysis. By evaluating the energy dissipation and displacement demand of the strengthened frames, the study seeks to provide insights into the efficacy of KBRBs as a retrofitting solution. The significance of this research lies in tackling a key gap in retrofit strategies for vulnerable reinforced-concrete structures and introducing a promising approach for improving the seismic performance of deficient frames. The findings from this study will contribute to the development of performance-based design guidelines, enabling engineers to select effective retrofitting strategies that safeguard infrastructure in earthquake-prone regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".