Analysis of Retrofitted Concrete Columns using 3D Elastic-Plastic Modeling
Bibliographic record
Abstract
Fiber Reinforced Polymer (FRP) jacketing is frequently used to enhance the performance of concrete columns. The strength and ductility of a concrete column increase after FRP jacketing mainly because of concrete’s improved behaviour under confinement. To achieve a reliable retrofit design, engineers often need to understand how much the performance is improved after FRP jacketing. For that purpose, numerical modelling is often necessary. Phenomenological elastic-plastic models are widely used for the numerical modelling of concrete because of their capability of representing 3D concrete behaviour considering permanent inelastic deformations. This research aims to implement an elastic-plastic model to simulate FRP-jacketed concrete columns. The 3D material model is validated against existing experimental data and comparisons with the results of models developed in ABAQUS software. It is shown that the proposed modelling approach is capable of providing an accurate behaviour of square concrete columns confined with reinforcements and FRP jackets under compression. After the validation of the model, a parametric study is conducted to illustrate the consequence of partial wrapping on the behaviour of retrofitted square concrete columns and to test the effect of FRP and concrete properties on the confined concrete strain. The obtained results from the parametric study are then used to acquire a suitable confined concrete strain equation/formula using a nonlinear regression technique. The core of this technique is based on an optimization method that finds the optimal coefficients for the proposed equation, helping in choosing the equation with the best fitting performance to the confined strain that is gained from the 3D material model. This proposed equation is recommended to improve the Canadian Standards by describing the impact of material properties on the confined concrete strain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".