Characterization of interface properties for modeling the shear behavior of T-beams strengthened with ultra high-performance concrete
Bibliographic record
Abstract
The application of Ultra high-performance concrete (UHPC) lateral layers on normal-strength concrete (NSC) beams substantially improves their shear performance. However, the impact of the NSC-UHPC interface properties on the shear performance has been scarcely studied and may hinder the strengthening efficiency and modify the failure mode. This study has characterized the complete shear and tensile behavior of interface specimens and evaluated the effect of anchors at the interface. Then, the complete curves of the interface allowed calibration of an interface concrete fracture (CF) model that was introduced in finite element (FE) simulations replicating the shear behavior of UHPC-strengthened T-beams tested in the same project. Parametric studies were then made with FE simulations. Results showed the FE simulations with the CF model better capture the stiffness evolution and failure mode of strengthened beams than that with the perfect bond model. The increase of UHPC layer thickness up to 80 mm and anchor spacing at the interface smaller than 300 mm considerably increased the stiffness and shear capacity of the strengthened beams. Existing dead load on beams did not impair UHPC strengthening and a substantial increase in shear capacity can still be achieved in real-size strengthened T-beams.
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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) | 0.001 | 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) | 0.001 | 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".