Experimental and numerical investigations of the effectiveness of engineered cementitious composites and stainless steel plates in shear strengthening of reinforced concrete beams
Bibliographic record
Abstract
Abstract This paper investigates the shear strengthening of reinforced concrete (RC) beams incorporating engineered cementitious composite (ECC) and stainless steel plates (SSPs). The use of ECC, characterized by strain‐hardening in conjunction with SSPs, was investigated in this study to improve the shear performance of RC beams. Total 10 RC beams were tested under static loading up to failure to investigate a few key parameters, namely: material of strengthening (ECC and SSPs), the thickness of ECC, and shape and configuration of SSPs. Experimental findings showed that the proposed strengthening methods can significantly improve the failure pattern and increase the ultimate shear capacity of the studied RC beams by 36%–97% compared to the unstrengthened beam. Experimental results were compared against the predicted ultimate shear strength of RC beams using design equations specified by various design codes. Nonlinear three‐dimensional finite element modeling was developed for beams strengthened with ECC layer and validated against the test results and found to be accurate. Based on the experimental and numerical results, new shear capacity formulae were proposed considering the ratio of ECC‐to‐concrete beam cross‐section ( ρ ECC ) and then verified against the numerical predictions.
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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".