Torsional Strengthening of RC Beams with Openings Using Hybrid SHCC–Glass Fiber Mesh Composites
Bibliographic record
Abstract
This study investigates the effectiveness of strain-hardening cementitious composites (SHCC) reinforced with glass fiber (GF) mesh in enhancing the torsional behavior of reinforced concrete (RC) beams with circular openings. Eight full-scale RC beams were tested under pure torsion, including two control beams and six strengthened beams with varying configurations of horizontal, vertical, and combined SHCC-GF mesh retrofitting. The experimental program evaluated the influence of single- and double-layer GF mesh reinforcement on torsional capacity, crack propagation, stiffness, and energy absorption. The results demonstrated that the presence of an opening reduced the ultimate torsional capacity by 29%, elastic stiffness by 48%, and energy absorption by 64% compared to the solid control beam. Strengthening with horizontal SHCC strips restored 21–35% of the lost capacity, while vertical strips performed even better, achieving 44–61% improvement. The combined horizontal–vertical configuration with a double-layer GF mesh proved the most effective, increasing ultimate load by 91% compared to the unstrengthened beam with an opening. Finite element models (FEM) are developed using ABAQUS to simulate the performance of the tested beams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".