Transition of shear failure mechanism for non-stirrup ultra-high performance concrete (UHPC) beams: Experiments and predictions
Bibliographic record
Abstract
This study investigates the transition of shear failure mechanisms in non-stirrup ultra-high-performance concrete (UHPC) beams, addressing a critical gap in understanding how shear transfer evolves with varying shear span-to-depth ( a/d ) ratios. Six full-scale UHPC T-beams with a/d ratios ranging from 1.5 to 4.0 were tested to examine their failure mechanisms, cracking behavior, deflection response, shear capacity, and strain distribution. In the analytical phase, the accuracy of three sectional shear capacity models—FHWA-23, PCI-22, and Foster and Bentz (2024)—was evaluated, and two strut-and-tie models (STMs) were developed and validated against experimental results to capture shear transfer mechanisms across different a/d ratios. The results showed that while the sectional models, originally developed for beam-action-dominated regions, provide conservative estimates, their accuracy declines significantly at lower a/d ratios where arch action governs. Based on both experimental and analytical findings, a transition point at a/d = 3.3 is proposed for non-stirrup UHPC beams, beyond which sectional analysis becomes applicable as arch action diminishes. These findings enhance the understanding of UHPC shear behavior and support the development of more rational and reliable design guidelines for UHPC structures. • Investigates shear failure transition in non-stirrup UHPC T-beams. • Tests six full-scale beams with a/d ratios ranging from 1.5 to 4.0. • Develops and validates two STMs to model shear transfer mechanisms. • Existing sectional models lose accuracy as arch action becomes dominant. • Proposes a shear transition point at a/d = 3.3 for non-stirrup UHPC 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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".