Size Effects in Flexural Toughness of Fiber Reinforced Concrete
Bibliographic record
Abstract
Abstract The influence of specimen size on measured flexural toughness of fiber reinforced concrete (FRC) remains largely unknown. Towards this end, an experimental program was carried out where flexural toughness tests were performed on specimens of two different sizes using ASTM C1609/C1609M-07 test procedure. Two specimen sizes of 100×100×350 mm3 and 150×150×500 mm3 were tested. A hybrid polymeric fiber at three fiber dosage rates of 3.0, 4.5, and 6.0 kg/m3 was investigated. Results indicate that the specimen size affects not only the toughness properties but also the variability in data as quantified by the coefficient of variation (COV). Size effect was more pronounced in the early part of the load-deflection curve and decreased with an increase in the fiber volume fraction. At a given fiber volume fraction, the COV, moduli of rupture, and post-crack residual strengths were all higher for the specimens of smaller size. Finally, the small size specimens showed a greater deflection hardening than the large size specimens. The last conclusion is significant in that deflection hardening is now often cited as the required performance characteristic of high performance FRC, and this property may depend more on the size of the specimens than on the composite itself.
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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 | 0.004 |
| 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".