State of Research on Seismic Retrofit of RC Beam-Column Joints with Externally Bonded FRP
Bibliographic record
Abstract
A considerable amount of research has been directed recently toward understanding and promoting the use of externally applied fiber-reinforced polymer (FRP) for the seismic retrofit of reinforced concrete (RC) structures. In this paper, a comprehensive review and synthesis of published experimental studies on the seismic rehabilitation of RC frame beam-column joints with FRP is presented, and the issues that need to be addressed for further research are discussed. In addition, the paper presents a simple design model for predicting the contribution of the FRP to the shear strength of retrofitted joints. The key element in the model is the derivation of an expression for the effective FRP strain, based on the calibration of test data reported in the literature. A total of 54 tests carried out worldwide were considered in the review, and a database of the published studies, encompassing all relevant design parameters, was assembled. The reported test results confirm the structural effectiveness of the FRP strengthening technique for the seismic retrofit of RC joints. However, there are some gaps which need to be addressed. For instance, there is a lack of a rationale explanation of the resistance mechanisms involved in the beam-column joints retrofitted with FRP. Such a rational explanation is a prerequisite for the development of more comprehensive and rigorous design procedure.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".