Shear Strength of Hybrid Fiber Reinforced Self-Compacting Concrete Beams
Bibliographic record
Abstract
Self-compacting concrete has constructive advantages over conventional concrete, such as reducing labor and construction time, mainly because of its fluidity in the fresh state. However, in the hardened state, it maintains low performance when tensioned, and the fibers can be added to the mixture, maintaining a portion of the resistance after cracking. Steel fibers are usually added to concrete, but recently synthetic fibers have been used, due to their lower cost and non-corrosive nature, but with lower tensile strength. Thus, by combining the two types of fibers, the benefits of each material can be used. This work presents the results of an experimental program to evaluate the effect of the hybridization of metallic and synthetic fibers on the shear strength of self-compacting concrete beams without stirrups. The results demonstrate that both steel and hybrid fibers result in greater shear strength compared with the reference concrete without fibers before shear crack formation; however, the greatest advantages are attributed to post-cracking residual strength. The experimental results were compared with estimates calculated using equations published in the literature, demonstrating the feasibility of using some existing equations for concretes with the addition of hybrid fibers.
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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) | 0.000 | 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".