Experimental and Numerical Investigation of Shear Strengthening of Simply Supported Deep Beams Incorporating Stainless Steel Plates
Bibliographic record
Abstract
In this study, the effectiveness was investigated of shear strengthening techniques in reinforced concrete (RC) deep beams incorporating stainless steel plates (SSPs). Four RC deep beams were tested under incremental static loading until failure to examine the proposed strengthening techniques. The key parameters considered in this study included the arrangement of the externally bonded SSPs. The experimental findings demonstrated that strengthening using SSPs led to substantial improvements in their performance compared to the unstrengthened control beam. The use of SSPs increased the ultimate shear capacity by 129 to 175% over the control specimen. Finite element models (FEMs) were developed to simulate the responses of the tested beams strengthened using SSPs. Parametric studies were then conducted using the validated FEM to investigate to identify the effects of the area of SSPs on the shear capacity of the beams. The parametric studies concluded that increasing the plate thickness resulted in the enhanced shear capacity of the deep beam specimens up to a critical point upon which the increases in the thickness have insignificant effects on the shear strength. The accuracy of the design equations given by European and American codes in predicting the shear strength of the deep beams is examined.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".