Ductility and Strain Behaviour of Ultra-high Performance Fiber Reinforced Concrete Beam Containing Coarse Aggregate under Shear Load
Bibliographic record
Abstract
In this present paper, four point loading configuration was used to assess the effect of steel fibre volume (Vf), type of steel fibre, presence/absence of stirrup and shear span to depth ratio (a/d) on the ductility and strain behaviour of the UHPFRC-CA beam.Findings from the study revealed that a/d has the most influence on the ductility of UHPFRC-CA beam as it favours the development of new cracks, propagation of existing cracks and the realization of high shear capacity.Vf of up to 2% improves UHPFRC-CA beam's ductility beyond which leads to ductility reduction.Shear reinforcement in form of stirrups does not have significant impact on the ductility of UHPFRC-CA beam.UHPFRC-CA beam has lower compressive strain and higher tensile strain than the compressive strain and tensile strain of its cube specimen and dogbone specimen respectively.The use of hooked-end steel fibre and increase in Vf from 2% to 3% in the beam resulted in strain reduction in the compression zone of the UHPFRC-CA beam.The strain in the tension zone of UHPFRC-CA beam increased with the use of hooked-end steel fibre, increase in Vf from 2% to 3%, exclusion of stirrups and decrease in a/d from 2.82 to 2.08.The shear zone of UHPFRC-CA beam is characterized with low strain growth rate from the appearance of diagonal crack to failure; and the shear zone of UHPFRC-CA beam experienced strain reduction with the use of hooked-end steel fibre and increase in Vf from 2% to 3%.Finally, findings from the ductility and strain behaviour of this UHPFRC-CA beams can be utilized during research design and experimental stages to forecast the crack pattern and failure mode of UHPFRC-CA beam in terms of concrete crushing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".