Influence of fibre type on the shear behaviour of engineered cementitious composite beams
Bibliographic record
Abstract
The shear behaviour of large-scale engineered cementitious composite (ECC) beams reinforced with different types of fibre was evaluated. Four types of fibre were used: 8 and 12 mm long polyvinyl alcohol fibres (PVA8 and PVA12), 19 mm long polypropylene fibres (PP19) and 13 mm long steel fibres (SF13). An additional normal concrete (NC) beam of comparable compressive strength was cast and tested for comparison. The performance of all the test beams was evaluated through their load–deflection curves, cracking behaviour, first crack load, diagonal crack load, ultimate load, ductility and energy absorption capacity. The ultimate capacity and cracking moment of all the test beams were also compared with theoretical values estimated by some design code equations. The results indicated that, compared with the NC beam, all the ECC beams showed better performance in terms of cracking behaviour, shear capacity, ductility and energy absorption. The ECC beam reinforced with PVA8 fibres showed the highest shear strength and ductility of all the ECC beams with other polymeric fibres. Longer PVA fibres appeared to be less efficient than shorter ones. The beam reinforced with PP19 showed the lowest performance, while the use of SF13 proved to be the most effective in improving the first crack load, ultimate load, ductility and energy absorption capacity.
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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) | 0.000 | 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".