Shear and Flexural Behavior of Reinforced Concrete Deep Beams Strengthened with CFRP Composites
Bibliographic record
Abstract
The current research studies the shear and flexural behavior of reinforced concrete (RC) deep beams strengthened with externally bonded carbon fiber-reinforced polymers (CFRP). Using two types of CFRP composites including sheets and laminates, different configurations for shear and flexural strengthening of deep beams were experimentally investigated. In total, twenty specimens of deep beams with cross-sectional dimensions of 190 mm width, 400 mm depth and an overall length of 1 900 mm were casted and tested to failure. Concerning the cracks’ formation, failure’s modes, ultimate strength and overall stiffness, the performance of the strengthened beams compared to the control beams were evaluated. From the test results, the effectiveness of CFRP technique on enhancing both the shear and flexural capacity of deep beams is verified; however, the efficiency differs variedly depending on the material and the strengthening scheme. Regarding the shear strengthening, using the continuous wrap of two sheets records the highest increase in the ultimate strength with a value exceeds 86% compared only to 36% with the inclined laminates. On the other hand, an enhancement of about 51% is achieved through the flexural strengthening with two layers of sheets and 26% when the laminates are used; both are accompanied by a divergent in the failure mode from flexure to shear.
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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".