Investigation of Strut-and-Tie Model Performance Using Symmetrically Loaded GFRP–RC Double Corbels
Bibliographic record
Abstract
Corbels are characterized as low shear span-to-depth ratio (a/d) members that transfer vertical and horizontal loads to adjacent members such as columns or walls. Glass fiber–reinforced polymer (GFRP) reinforcement has linear-elastic behavior and a lower modulus of elasticity relative to steel leading to deeper and wider cracks, which is especially critical for low a/d members. Currently, Canadian bridge and building standards provide strut-and-tie modeling provisions to design steel– and GFRP–reinforced concrete (RC) corbels, but the United States (US) code for GFRP–RC structures prohibits the use of this method for GFRP–RC corbels because of lack of research. Eight full-scale GFRP–RC corbels were constructed and tested to failure. The test variables included main tie and secondary reinforcement ratios, a/d ratio, and concrete strength. The experimental results indicated that a/d and concrete strength have a considerable influence on concrete crack width development, deflection, and load-carrying capacity. The Canadian standard for FRP–RC buildings provided conservative shear capacity predictions for all eight corbels. The US code for steel–RC structures overestimated the shear capacity predictions for seven of the eight corbels, suggesting that revisions are required to better predict the capacity of GFRP–RC corbels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".