Numerical Simulation and Experimental Testing of Concrete Beams Strengthened in Shear with Fabric-Reinforced Cementitious Matrix
Bibliographic record
Abstract
This paper presents results of finite element (FE) modeling and experimental testing of reinforced concrete beams strengthened in shear with fabric-reinforced cementitious matrix (FRCM). The studied parameters included the number of FRCM layers (one and two layers), matrix type (mortar and epoxy), and amount of internal stirrups (no stirrups and stirrups with spacings of 0.6d and 0.3d, where d is the depth of the tensile steel). Test results showed that the shear-strength gain after strengthening was in the range of 51–145%. The shear-strength gain decreased with an increase in the amount of internal stirrups. Doubling the number of FRCM layers resulted in a nonproportional increase in the shear strength. The use of epoxy adhesive rather than a cementitious mortar as a matrix insignificantly increased the shear-strength gain. The effect of increasing the amount of FRCM or varying the matrix type on the shear strength was less pronounced for the specimens with internal stirrups. The FE models developed in this study were capable of predicting the nonlinear shear response of the tested specimens. A comparison between predicted and experimental results confirmed the accuracy and validity of the developed FE models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".