Innovative Wedge Anchorage for CFRP Plates: Development and Testing
Bibliographic record
Abstract
Gripping prestressed carbon fiber-reinforced polymers (CFRPs) in structural strengthening applications is challenging due to CFRPs’ susceptibility to lateral loading. This paper presents a reliable and reusable wedge anchorage for gripping CFRP plates that are 50 mm wide and 1.2 mm thick. The cylindrical anchorage, which is 75 mm long and 76.2 mm in diameter, consists of an external steel barrel, two internal steel wedges, and two soft copper sleeves. The barrel-wedge interface is designed using an innovative arc–linear configuration, through which the desired stress distribution is attained, preventing stress concentration and the premature failure of the CFRP plate. The wedge anchorage was experimentally tested by applying a displacement-controlled tensile load of 0.6 mm/min until the complete fracture of the CFRP plate. The anchorage’s performance was examined under distinct installation conditions by applying different presetting levels: high (40–120 kN) and low (hammering) presetting. It was observed that the anchorage successfully prevented CFRP premature failure in all tests by achieving an average tensile loading of 172.3 (±5.7) kN, exceeding its reported tensile strength of 168 kN (2800 MPa). Minor CFRP displacements of 6.26 (±0.75) mm and 3.33 (±0.16) mm were recorded under low and high presetting levels, respectively. Similarly, the CFRP slippage relative to the wedges for the low and high presetting tests was only 1.18 (±0.75) mm and 0.33 (±0.15) mm, respectively. Also, only minor scratches were observed in the wedge–barrel interface, indicating the absence of extensive plastic deformation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".