Partial factor for the shear resistance model in the 2nd generation of Eurocode 2 for GFRP reinforced concrete members
Bibliographic record
Abstract
Fibre-reinforced polymer (FRP) bars provide a useable alternative to conventional steel reinforcement in concrete structures exposed to chlorides from de-icing salts and marine environments. Glass fibre-reinforced polymer (GFRP) bars have the widest practical applications in concrete structures all over the world. The lower modulus of elasticity and bond with concrete compared to steel rebars led to the development of new or modification of existing design models for predicting the shear capacity. The 2nd generation of Eurocode 2 (prEC2) introduces a new design provision for predicting the shear capacity of concrete members reinforced with FRP bars together with a new partial factor for shear γ V . The paper deals with the statistical analysis of the accuracy and reliability of this design model. The analysis was carried out using a database of 288 experimental tests on beams and one-way slabs reinforced with GFRP bars. The correction factor b cor of a model and the coefficient of variation of the model error V δ were evaluated according to Annex D of the EN 1990. Probabilistic properties of the random variables in the resistance function were assumed according to the recommendations of the JCSS Probabilistic Model Code and values introduced in Annex A of the prEC2. The analysis showed acceptable accuracy with Cov of model uncertainty V θ = 0.190. However, the partial factor γ V ranged within the interval of 1.62 to 1.65 which is a higher value than the proposed normative value of 1.50. The lower safety of the model also confirmed the comparison of the results with the performance of the design provisions in Canadian standard CSA S806-12 and in the ACI code of practice ACI 440.1R-15. An adjustment of the prEC2 design provision was proposed with the aim to increase the reliability of the model and maintain the normative value of the partial factor for shear on a proposed value of 1.50.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".