Experimental Study on the Shear Behavior of UHPC-Strengthened Concrete T-Beams
Bibliographic record
Abstract
Ultrahigh-performance concrete (UHPC) strengthening is an efficient technique to improve the capacity of shear-deficient members. However, the applicability of UHPC strengthening on a T-beam has been scarcely investigated, particularly with regard to repair configurations not reaching beam supports involving a higher delamination potential. In this study, the shear behavior of concrete T-beams with cast-in-place UHPC strengthening is investigated with 10 concrete T-beams, including different strengthening configurations, layer thicknesses, and anchors at the repair interface. Beams with a UHPC bottom layer ending before the support are investigated for the first time. Load–deflection, lateral, and cross-sectional cracking patterns in each beam are analyzed. Besides, strain distributions of the beams are recorded and analyzed through a digital image correlation system to distinguish different failure modes. The efficiency of UHPC strengthening for improving the shear behavior of concrete beams is clarified, and recommendations are provided for a UHPC-strengthened beam design to avoid delamination. The testing results show that UHPC strengthening using lateral layers is the most efficient configuration for improving shear capacity and does not increase the sectional height, while a U-shaped jacket configuration is recommended when a substantial increase of beam stiffness is required. A combination of shear and separation failure is found in beams with a UHPC bottom layer ending before the support, and it is, therefore, suggested not to use a UHPC bottom layer alone due to the development of separation cracks. More ductile failure modes obtained with 50-mm lateral layers and a 25-mm U-jacket are suggested. Installation of anchors at the repair interface is recommended to achieve more ductile failure modes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".