Structural Reliability of GFRP-RC Slab Column Connections Based on a Data-Driven Robust Algorithm: Punching Shear Limit State
Bibliographic record
Abstract
This study focuses on the assessment of the structural reliability of the punching shear limit state in five distinct configurations of glass fiber–reinforced polymers (GFRP)–RC slab-column connections, illustrating their diverse applications through a robust data-driven method. The Nelder–Mead simplex minimization algorithm is used to fit an efficient predictive model for the ultimate punching shear capacity. In the context of the reliability analysis, considered variables include the effective depth, GFRP reinforcement area, concrete compressive strength, GFRP modulus, dead loads, live load, and errors in the mechanical model. The data set, forming the basis for the data-driven model, is derived from numerical simulations conducted using an FEM based on a concrete damaged plasticity model, which was first validated with sixteen experimental observations from the literature. Structural reliability is also assessed using the first-order reliability method in accordance with commonly used design provisions for GFRP-reinforced concrete structures in North America. The results indicate that the provisions based on US standards generally lead to reliability indices above 3.0, while those following Canadian standards result in indices below this threshold in a significant proportion (40%) of cases.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".