The Effect of Casting Flow Defects on the Flexural Behavior of 2-way UHPFRC Slabs Investigated by Digital Image Correlation and Magnetic Assessment
Bibliographic record
Abstract
Structural applications of Ultra High Performance Fiber Reinforced Concrete (UHPFRC) have been emerging worldwide thanks to the outstanding tensile strength and ductility of this category of materials. From a design point of view, a critical aspect is to guarantee the ductility of the structural response associated to local material non-brittleness, which strongly depends on the fiber dispersion and orientation. This work aims at considering the effect of fiber orientation on the biaxial behavior of UHPFRC slabs for composite bridges. Several square slabs made of UHPFRC were cast by changing the concrete flow direction and the position of internal cold joints. The slabs were tested under biaxial bending under hyperstatic conditions. The fiber orientation was measured by a non destructive method based on the magnetic properties of the composite, while the damage process, with emphasis on the microcrack formation and the crack opening, was measured by 3D digital image correlation. Finally, approaches based on the yield line method were employed to analyze the experimental results with respect to the measured fiber orientation and crack distribution. The results of the present work show the important effect of fiber orientation on the ductility of UHPFRC slabs and the ability of combining magnetic methods and Digital Image Correlation (DIC) analyses to capture those effects.
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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".