Low-cost and ductile prestressed UHPC beams with hybrid reinforcement: Experiments and design methods
Bibliographic record
Abstract
Ultra-High Performance Concrete (UHPC) is a modern class of cementitious composite materials. With superior mechanical properties and durability, it has gained increasing structural applications worldwide. The current design of prestressed UHPC beams commonly fails due to the fracture of prestressing strands quickly after the crack localization of UHPC, rather than after the crushing of the cementitious matrix as expected in traditional concrete beams. When failing quickly after crack localization, the high compressive strength of UHPC is not fully utilized and beams exhibit low structural ductility and inadequate safety warnings before failure (i.e., nearly invisible cracking and compressive damage). To address these challenges, this study develops a novel ductile design method for the prestressed UHPC beams by introducing secondary reinforcement (mild steel or fiber-reinforced polymer bars), forming a hybrid reinforcement scheme. Four-point bending tests are conducted on four full-scale beams: two beams represent the current practice in China and the state-of-the-art design in the US, while the other two represent the proposed new design method. Test results demonstrate that the proposed design presents a ductile failure after the formation of multiple localized cracks and can exhibit significant UHPC crushing, providing many failure warnings. Compared with currently common design, the proposed design can improve the peak load and deflection capacity by up to 27 % and 109 %, while reducing the factored cost-to-strength ratio by 28–39 %, respectively. A failure path prediction method is developed and validated for prestressed and non-prestressed UHPC beams with various types of longitudinal reinforcement.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".