Predicting the effect of non-uniform fiber distribution on the tensile response of ultra-high-performance fiber reinforced concrete by magnetic inductance-based finite element analysis
Bibliographic record
Abstract
on-uniform fiber distribution can significantly reduce the extension of multiple-cracking and favor crack localizations in Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) members under tension with important implication on the sought durability. This work aims at fostering the coupling between a novel Non-Destructive Technique, namely Magnetic Inductance Method (MIM), and Finite Element Method (FEM) to predict the effect of nonuniform fiber distribution on the micro-cracking response of UHPFRC samples under tensile loading. First, uniaxial tensile tests on 5 dumbbell samples of UHPFRC with 3.8% of steel fibers showed that tensile ductility is much affected by the degree of uniformity of the fiber distribution. Thus, FEM analysis was performed with the Concrete Damaged Plasticity model (CDP) in Abaqus software, where the UHPFRC tensile law was scaled by a field variable based on the fiber orientation factor and the fiber efficiency factor (μ0 and μ1) measured by Magnetic Inductance Method (MIM). The field variable scales the UHPFRC tensile law between an upper and a lower bound of the tensile law estimated by a fiber pull-out model and a cohesive law for concrete matrix, respectively. The accuracy of the proposed MIM-FEM method was verified against the experimental results by considering the load-displacement curve, the asymmetric displacement, the crack pattern, the fracture energy, and the evolution of the microcrack opening. Based on the presented results, the proposed MIM-FEM method can map and quantitatively analyze the effect of non-uniform fiber distribution for UHPFRC members under tension, thus providing potential application value for infrastructures, pre-casting and architectural applications more broadly.
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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 | 0.001 |
| 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) | 0.000 | 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".