Finite element analysis of reinforced concrete columns confined with composite materials
Bibliographic record
Abstract
The use of composite materials in civil engineering, especially for the strengthening and retrofitting of existing structural elements, is a domain that is growing at a fast pace. The rapid expansion of the structural repair business has already provided numerous opportunities to demonstrate the potential of these materials. However, it has also indicated the need to better understand their properties and the necessity of reliable models to predict the behaviour of repaired structures and the long-term response of those materials considering the effects of freeze and thaw or UV exposure. This article presents a model to predict the ultimate load of a reinforced concrete column confined with composite materials without post-peak response. In addition to the ultimate load, the model identifies the evolution of stresses and strains in the column during the entire loading process. Calculations are made under constant axial and incremental lateral load. The finite element calculations presented here are based on the use of bar elements. These are numerically integrated, considering the appropriate behaviour of the materials in the section of the column. Special attention was paid to the development of a stress-strain relationship representative of the actual behaviour of concrete confined with composite materials.Key words: composite materials, reinforced concrete column, repair, modelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".