Finite-element modelling of S<b>F</b>RC members in bending
Bibliographic record
Abstract
This paper is aimed at understanding the mechanics of steel-fibre-reinforced concrete (SFRC) in the context of designing for structural applications. It focuses on the testing procedures adopted to obtain the tensile response of SFRC that are used in finite-element models of structural elements submitted to bending. Modelling of standardised material test specimens enabled validating the assumptions used in inverse analysis to determine the post-cracking σ–w response from bending tests on notched beams and round panels. The effect of fibre orientation, the testing procedure and the validity of standardised test are discussed. Modelling of SFRC structural beams of different scales, shapes, with and without conventional reinforcement, emphasises the importance of using non-uniform material properties within the model to correctly predict the member stiffness and strength, and the crack opening evolution. The paper confirmed that the integration point spacing must be used as the reference length for converting σ–w post-cracking response to σ−ε material properties for carrying out finite-element analysis. Moreover this approach is not affected by the element size and member depth.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".