Experimental and MCFT-Based Study on Steel Fiber-Reinforced Concrete Subjected to In-Plane Shear Forces
Bibliographic record
Abstract
Abstract Testing of concrete panels subjected to pure in-plane shear loading is necessary to elucidate the shear behavior of concrete. However, available data for predicting the shear capacity and behavior of steel fiber-reinforced concrete are rather limited. This study aims to evaluate the shear capacity and behavior of fiber-reinforced concrete made of highly flowable strain hardening fiber-reinforced concrete (HF-SHFRC) experimentally and analytically, respectively, using a panel tester loaded under pure shear and modified compression field theory (MCFT). The test was conducted using a panel test machine at the University of Toronto. The test results of the HF-SHFRC demonstrated strain hardening behavior at tension after the first crack, as indicated by the increase in the shear stress after the first crack in the HF-SHFRC panel. An analysis procedure is proposed for predicting the shear strength of steel fiber-reinforced concrete (SFRC) based on experimental data of the SFRC panels to obtain reliable results. A comparison of results obtained from the proposed analysis procedure and experiments show that it accurately predicted the response of the HF-SHFRC. The proposed MCFT-based analysis procedure can provide valuable insight for understanding the behavior of the SFRC panels under shear loading.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".