Modeling the flexural strength of steel fibre reinforced concrete
Bibliographic record
Abstract
Industrial applications of fibre-reinforced concrete (FRC) in structures require extensive experimental and analytical investigations of the FRC material properties. For design purposes and applications involving the flexural loading of the member, it is essential to have a predictive model for the flexural strength of the FRC material. In the present paper, a fracture mechanics approach based on Bridged Crack Model (BCM) is used to predict the flexural strength of steel fibre-reinforced concrete (SFRC) beams. The model assumes a quadratic tension-softening relationship (σ-δ) governing the bridging action of the steel fibres and a linear profile of the propagating crack. The proposed tension-softening relationship is considered valid for a wide range of fibre-reinforced concrete materials based on the knowledge of either the material micromechanical parameters (such as fibre volume fraction, fibre/matrix bond strength, fibre length, and fibre tensile strength) or an actual experimentally-measured σ-δ relationship. The flexural strength model thus obtained allows the prediction of the flexural strength of SFRC and study the variation of the latter as a function of the micromechanical parameters. An experimental program involving the flexural testing of 13 SFRC prism series was carried out to verify the prediction of the proposed model. The SFRC mixes incorporated two types of steel fibres (straight-end and hooked-end), four different concrete compressive strengths (40, 50, 60, and 70 MPa), three different fibre volume fractions (1, 1.5, and 2%), and three specimen depths (100, 150, and 200 mm). The experimental results were compared to the predictions of the proposed flexural strength model, and a reasonable agreement between the two has been observed. The model provided a useful physical explanation for the observed variation of flexural strength as a function of the test variables investigated in this study.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".