Performance Evaluation of Current Design Models in Predicting Shear Resistance of UHPC Girders
Bibliographic record
Abstract
This manuscript delivers a comprehensive evaluation of five different ultra-high-performance concrete (UHPC) shear resistance models: FHWA-HRT-23-077 (2023), ePCI report (2021), French Standard NF-P-18-710 (2016), Canadian Standards A23.3-04 (2004), and Modified Eurocode2/German DAfStb (2023). The models differ in accounting for the steel fiber and shear reinforcement contribution and determining the angle of inclination of the diagonal compression strut. The evaluation was carried out using an experimental database of 198 UHPC specimens and focused on accuracy, conservatism, and ease of use for each considered model. The database included beams with prestressed and steel reinforcement, different shear reinforcement ratios, and a wide range of geometrical and material properties. In order to apply the FHWA method, a utilization tensile stress (ft,loc) prediction equation was developed. Generally, the FHWA method showed superior performance to the other models in terms of statistical measures and consistent prediction conservatism across variable ranges. Although the ePCI methods yielded the highest conservatism, it can be said that the ePCI, AFGC, and CSA methods showed similar behavior with different degrees of conservatism. The DAfStb method showed the lowest prediction accuracy and the greatest scatter of data. Except for the FHWA method, all methods showed a reduction in conservatism at a high transverse reinforcement ratio.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".