Flexural Strengthening of Concrete Structures Using Externally Bonded and Unbonded Prestressed CFRP Laminates: A Literature Review
Bibliographic record
Abstract
Carbon fiber-reinforced polymer (CFRP) materials have been widely used in prestressed and non-prestressed systems for the flexural strengthening of concrete structures in many construction projects worldwide. Strengthening with non-prestressed CFRP materials, which is known as passive repair, could lead to an increase in the ultimate capacity of the concrete member. However, it has little effect on its serviceability performance (e.g., cracking, yielding, and deflection properties). Flexural strengthening with prestressed CFRPs provides an active repair technique that positively effects the strength and serviceability of structures. In addition, strengthening with prestressed fiber-reinforced composites efficiently uses the material’s high tensile strength. Although different fiber-reinforced polymer (FRP) materials and forms have been used in prestressing applications, this paper focused on the use of CFRP laminates (e.g., sheets, strips, or plates). These laminates have been widely used due to their excellent mechanical properties, ease of application in various geometrical shapes, good conforming surface area of contact with structures, and low installation costs. A comprehensive literature review was presented on strengthening using prestressed CFRP laminates in conventionally and internally prestressed concrete structures (PCSs). The externally bonded and unbonded prestressed CFRP laminate applications that used different anchorage systems were presented. The flexural behavior, failure modes, and serviceability performance of the strengthened concrete members were discussed. The effects of the prestressing level on ductility and energy absorption and a summary of the recommended optimum prestressing level discussions were presented in this paper. Furthermore, a review of the theoretical studies that were conducted for prestressed CFRP applications was provided. The knowledge gaps in the research area and future research recommendations were presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".