Shear strengthening of normal concrete deep beams with openings using strain-hardening cementitious composites with glass fiber mesh
Bibliographic record
Abstract
Reinforced concrete (RC) deep beams are used in offshore platforms, bunker walls, and building load-bearing walls. Web openings are often necessary for accessibility or essential services. However, enlarging these openings due to architectural or mechanical requirements or changes in building function can significantly decrease shear capacity, posing a serious safety hazard to the structure. This study proposes a novel shear-strengthening technique of Reinforced concrete Deep Beams (RCDBs) with different opening shapes by incorporating Strain Hardening Cementitious Composites (SHCC) and Glass Fiber (GF) mesh. The shear behavior of RCDBs without and with different opening shapes is investigated experimentally and numerically. Six RCDBs were tested under static loading until failure to investigate two key parameters: opening shape (rectangular, square, and circular) and opening width-to-beam depth ratio (0.28 h and 0.20 h). The cracking force, crack patterns, observed deterioration modes, peaked shear force, load-vertical displacement, elastic stiffness, and absorbed energy capacities of the tested beams are reported. The experimental results showed that the utilized strengthening technique significantly increased the ultimate shear capacity, elastic stiffness, and absorbed energy, with the rate of increase decreasing with the increase of the opening size. The circular openings demonstrate a better performance than rectangular and square openings. A finite element model (FEM) was created using ABAQUS software to simulate the behavior of the tested NCCBs and validated against the experimental results. Good agreement was observed between the finite element simulations and experimental results, demonstrating that the FEM was able to accurately predict the shear behavior of RCDBs with SHCC and GF.
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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) | 0.000 | 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 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".