Post-Fire Behavior and Repair of Fire-Damaged RC Columns Using Composite Jackets
Bibliographic record
Abstract
Reinforced concrete (RC) structures are frequently employed in construction owing to their versatility, strength, and durability.However, these structures can be vulnerable to fire incidents, which can significantly compromise their structural integrity and loadcarrying capacity.In the aftermath of a fire, damaged RC columns often necessitate rehabilitation to restore their strength and functionality.The present study intends to carry out a numerical investigation of the behavior of reinforced concrete (RC) columns after their exposure to fire.As a first step, the study examined the effects of exposing the columns to fire for different periods (15, 30, 60 and 90 minutes) on the column's residual load-bearing capacity by considering some decisive geometrical parameters such as the column height and its cross-sectional area.The second step consisted of investigating the effectiveness of the strengthening techniques utilized by adding reinforcement and incorporating composite jackets, where each method used three external concrete compressive strength values, 25, 30, and 40 MPa, in order to improve the post-fire behavior of these columns.The results showed that the longer the column is exposed to fire, the lower its bearing capacity.However, it was also found that increasing the column cross-sectional area can reduce the percentage of load-bearing capacity.Moreover, A simple equation with sufficient accuracy has been proposed to predict the bearing capacity of reinforced columns.Finally, it was revealed that the strengthening methods used herein allowed restoring the capacity of the columns exposed to fire, but the strengthening technique using a composite jacket with steel plates showed better results in terms of strength.Where this technique allowed, it restored the capacity of the columns exposed to fire for a period of one hour by up to 182%.
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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) | 0.000 | 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 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".