Blind competition on the numerical simulation of slabs reinforced with conventional flexural reinforcement and fibers subjected to punching loading configuration
Bibliographic record
Abstract
Abstract This paper describes the 3rd Blind Simulation Competition (BSC) organized by the fib WP 2.4.1 which aims to assess the predictive performance of models based on the finite element method (FEM) for analysis and design of fiber reinforced concrete (FRC) structures submitted to loading and support conditions that promote punching failure mode. Fiber reinforcement is used in an attempt to eliminate conventional punching reinforcement and provide technical and economic advantages. The two tested real‐size prototypes represent a column‐slab interior region of an elevated steel‐fiber reinforced concrete (E‐SFRC) slab where anti‐progressive collapse reinforcement is disposed in the alignment of columns/piles. Despite a punching failure surface being formed in both experimentally tested prototypes at the rupture stage, fiber reinforcement was able to mobilize the yield capacity of the conventional flexural reinforcement, providing high deformation capacity, and ductility to the prototypes. The average post‐peak load‐carrying capacity of the tested prototypes at a deflection seven times higher than the deflection at yield initiation of the conventional reinforcement was still 90% of the average peak load. Regarding the BSC, a total of 26 proposals were received and involved 94 participants from 29 institutions and 17 countries, with 53.9% using smeared crack models (SCMs), 30.8% a concrete damage plasticity (CDP) model, 3.8% discrete crack models (DCMs) and 11.5% considered as “other models.” From these simulations it was verified, in average terms, that SCM assured the best predictive performance apart from the average strain in the SFRC and the maximum crack width which were better predicted by DCM. More accurate predictions were obtained by using in‐house software than by adopting commercial software.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".