Evaluation of <scp>ACI CODE</scp> 440.11‐22 design provisions for <scp>GFRP</scp> ‐ <scp>RC</scp> columns: Constitutive models and practical interaction diagrams
Bibliographic record
Abstract
Abstract With the release of ACI CODE‐440.11‐22, which introduces design provisions for GFRP‐Reinforced Concrete (RC) columns, it is important to evaluate the limitations of the adopted approach and provide practical design tools. This study develops and validates an analytical cross‐sectional model—based on equilibrium and strain compatibility—against theoretical and experimental data, to generate axial load–bending moment (N–M) interaction diagrams for short GFRP‐RC columns in accordance with the new code. The model uses the equivalent rectangular stress block for concrete in compression and incorporates key design parameters, including GFRP bar properties and strength reduction factors. To assess the conservatism of the code approach, the study compares interaction diagrams generated using the code's stress block with those developed using nonlinear concrete constitutive models. Results show that the code‐based diagrams are more conservative, and a scaling factor of 1.1 is suggested to better match actual behavior. As the ACI code neglects the compressive contribution of GFRP bars, their tensile strength affects only the tension side of the interaction diagram. Moreover, variations in the bar's elastic modulus influence the diagram from the balance point on the tension side up to the point where all bars are in compression and thus excluded from the analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".