Investigating the fracture toughness of the self compacting concrete using ENDB samples by changing the aggregate size and percent of steel fiber
Bibliographic record
Abstract
In reality, concrete structures are normally under various loadings, and results of different studies have shown that cracks in these structures and their materials, due to their nature as well as the loading type, do not develop along the crack plane (pure mode I); rather, they expand under mixed modes, making the crack growth studies under these modes a very important issue. In the crack growth phenomenon, the fracture toughness is a very effective parameter usually calculated by ENDB samples because they are easy to handle. In this study, several samples were made by changing the maximum aggregates size (dmax = 9.5, 12.5 & 19 mm) and the amount of hooked-end steel fibers (SF = 0.1, 0.3 & 0.5%), and tested under different loading modes (pure/mixed modes I and III) using the strain control jack device. According to the results, the lowest fracture toughness belonged to pure mode III, aggregates with dmax = 12.5 mm performed better in the self-compacting concrete reinforced with steel fiber, Also, the results show that the increasing trend of steel fibers does not have a positive effect on the fracture toughness performance.
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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 | 0.001 | 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".