Strengthening of reinforced concrete beams with circular openings under pure torsion using near-surface mounted GFRP and externally bonded CFRP
Bibliographic record
Abstract
The failure of reinforced concrete beams due to torsion is catastrophic due to the brittle nature of failure. Moreover, the presence of openings in the beams to facilitate building services may weaken RC beams in torsion. Thus, strengthening RC beams with openings in torsion may be required to ensure structural safety and integrity. This study investigates novel strengthening methods of RC beams with circular openings using Near-Surface Mounted (NSM) and Externally Bonded Reinforcement (EBR) techniques employing Glass Fiber Reinforced Polymer (GFRP) bars and Carbon Fiber Reinforced Polymer (CFRP) sheets, respectively to improve their torsional behavior. Nine full-scale RC beams were tested under torsion to evaluate the effects of the orientation of the CFRP sheets and GFRP bars (horizontal, vertical, and inclined), the size of the GFRP bars, and the combination of the application of NSM and EBR techniques. The effects of the proposed strengthening method on the crack patterns, torque–angle of twist behavior, torsion stiffness, and energy absorption capacity of the beams were examined. The results revealed that circular openings significantly reduced torsional capacity by 29%, elastic stiffness by 48%, and energy absorption by 64% compared to the master beam. However, side strengthening with inclined NSM GFRP bars and EBR CFRP sheets improved torsional capacity by 66% and 78%, respectively, over the defective master beam. The combined use of NSM GFRP bars and EBR CFRP sheets yielded the highest performance, increasing torsional capacity by 112%, and torsion stiffness by 159% compared to the defective master beam. Additionally, a 3D finite element model (FEM) using ABAQUS was developed to simulate the structural behavior of the strengthened beams, and validated against experimental results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".