Enhancing shear performance in UHPC beams through strategic fiber and stirrup integration
Bibliographic record
Abstract
This study pioneers a systematic experimental investigation into the combined effects of fiber volume and transverse reinforcement—both normal-strength steel (NSS) and high-strength steel (HSS)—on the shear performance of ultra-high-performance concrete (UHPC) beams, aiming to develop more efficient fiber–stirrup integration strategies. Twelve full-scale T-beams with varying fiber volumes (1 % and 2 %), transverse reinforcement types (NSS and HSS), transverse reinforcement ratios (0.4–0.7 %), and shear-span-to-depth ratios (1.7 and 2.7) were tested and evaluated using multiple performance indices, with results benchmarked against existing analytical models. Two alternative fiber–stirrup integration strategies were proposed and compared with the common practice of using 2 % fibers with or without stirrups: (1) reducing fiber content from 2 % (no stirrups) to 1 % with light NSS stirrups, and (2) replacing 2 % fiber beams with NSS stirrups by 1 % fiber beams with light HSS (MMFX) stirrups. Both alternatives maintained the shear resistance with 8–25 % lower costs, while also achieving finer crack control, smaller critical shear crack widths, and improved structural integrity. Collectively, these findings offer a pathway toward more economical and structurally resilient UHPC design practices, challenging conventional fiber–stirrup usage norms. • Shear resistance from fiber and HSS or NSS stirrup in UHPC beams. • 12 full-scale beams tested under varying fiber, stirrup, and a/d ratios. • Two proposed strategies matched shear capacity with up to 25 % lower cost. • Alternatives improved cracking, reduced CSC width, and enhanced integrity.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".