Seismic Retrofitting of Nonseismically Detailed Exterior Reinforced Concrete Beam-Column Joint by Active Confinement Using Shape Memory Alloy Wires
Bibliographic record
Abstract
It is widely accepted that the seismic retrofitting of nonseismically detailed reinforced concrete beam-column joints (BCJs) in reinforced concrete (RC) frame buildings is an urgent necessity due to its high vulnerability and potential consequences in seismic events. As a result, considerable effort has already been put into developing efficient and practical retrofitting solutions for such BCJs. Most of the existing techniques, however, are based on either passive confinement techniques, for example, fiber reinforced polymer (FRP) wrappings, or involve a considerable joint enlargement, which, in many cases, is undesirable. In this study a new technique of retrofitting BCJs is proposed by employing a more effective method of confinement (i.e., active confinement), utilizing the shape recovery feature of shape memory alloys (SMAs). To evaluate the performance of the proposed retrofitting scheme, experimental tests were conducted on full-scale BCJ specimens. The efficacy of the proposed retrofitting scheme is evaluated in terms of enhancement in strength, ductility, energy dissipation capacity, damage reduction in the specimens, and ease of application. The results from this study suggest that the proposed retrofitting scheme could be effectively used in achieving the full capacity of the joints corresponding to beam yielding and consequently enhances the energy dissipation capacity of the system significantly. The test results demonstrated that the proposed retrofitting scheme performs excellently in reducing the joint shear strain (core damage) (to almost zero or negligible) and also in retaining the full axial load carrying capacity of the column, even at very large drift values. The proposed retrofitted scheme could also be conveniently used in cases where the capacity ratio of column-to-beam needs to be improved.
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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 | 0.000 |
| 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 | 0.001 | 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 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".