Performance of corroded <scp>RC</scp> beam–column joints repaired using a hybrid scheme with <scp>HSFRC</scp> and stirrups replacement
Bibliographic record
Abstract
Abstract The present study proposes a new technique for retrofitting corroded beam–column joints (BCJs) using high‐strength fiber reinforced concrete (HSFRC) and stirrups replacement. The entire corrosion‐affected concrete was removed and replaced with HSFRC. The corroded reinforcing bars were cleaned and treated to resist the progression of the corrosion mechanism. The severely pitted stirrups were replaced with new stirrups. Four exterior BCJ specimens were tested under seismic loading to determine the effectiveness of the proposed retrofitting scheme. The efficacy of the proposed retrofitting scheme is determined in terms of the hysteresis response, stiffness degradation, cumulative energy dissipation, ductility, and damage index. A significant delay in the fracture of severely pitted reinforcing bars was experienced for the corrosion‐damaged retrofitted specimens compared to the corroded unretrofitted specimen. The cumulative energy dissipation of the corroded unretrofitted and corroded retrofitted specimens was 0.4 and 1.3 times that of the reference specimen, respectively, indicating the effectiveness of the retrofitting strategy, as both specimens had similar corrosion rates. The test results indicated that the proposed retrofitting technique effectively improved the seismic performance of the corrosion‐damaged BCJs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".