Shear Strengthening of RC T-Beams Using Mechanically Anchored Unbonded Dry Carbon Fiber Sheets
Bibliographic record
Abstract
This research studies the feasibility and effectiveness of a new method of strengthening existing RC T-beams in shear by using mechanically anchored unbonded dry carbon fiber (CF) sheets. This method eliminates the debonding of epoxy-bonded carbon-fiber-reinforced polymer (CFRP) sheets and utilizes the full capacity of dry CF sheets. In this method, dry CF sheets are wrapped around and bonded to two steel rods. Then the rods are anchored to the corners of the web-flange intersection of the T-beam with mechanical bolts. This makes a U-shaped dry CF jacket around the web which increases the shear strength of the T-beam using the privilege of higher tensile strength and modulus of elasticity of dry CF compared to composite CFRP. A total of three RC T-beams with shear span-to-depth ratio of 2.0 were tested under increasing monotonic load till failure. The pilot tests were done as a proof-of-concept of the effectiveness of the proposed method in increasing the shear capacity of the RC T-beams. The first T-beam, which was tested as the control beam, failed in shear. The second beam was strengthened by using a U-shaped CFRP sheet that was externally bonded to the web of the beam in the shear zones. The third beam was strengthened by using anchored U-shaped dry CF sheet. The test results showed that the beam strengthened by the new mechanically anchored dry CF had about 48% increase in shear capacity as compared to the control beam and 16% increase in shear capacity as compared to the beam strengthened by CFRP epoxy-bonding method.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".