A Numerical Investigation into the Effectiveness of Composite Jacket Strengthening (CJS) on Fire-Damaged Concrete Beams
Bibliographic record
Abstract
After a fire exposure, reinforced concrete (RC) structures typically retain their integrity, yet they incur significant damage due to material degradation and thermal expansion.The restoration of fire-damaged RC structures is a complex structural engineering challenge.This study presents a numerical investigation on the post-fire behavior of RC beams, subjected to parametric fire for different durations (15, 30, 60, and 90 minutes) and their retrofit method.Initially, the impact of high-temperature conditions on the residual load-bearing capacity is assessed, with a focus on beam length and support conditions as key geometric variables.Repair methods aimed at improving the post-fire performance of damaged beams are then evaluated.These methods include the use of additional reinforcement and the implementation of steel jacketing, complemented by concrete of varying compressive strengths (25, 30, 35, and 40 MPa).Eurocode models for both concrete and steel materials are used in the simulations using SAFIR software.Results indicate a decrease in load-carrying capacity with prolonged fire exposure, with capacity reductions reaching 85% for beams subjected to 90-minute fire scenarios.Application of steel jacketing markedly enhances both bending and shear resistance of the compromised beams, with the ability to restore the load-bearing capacity of one-hour fire-exposed beams by up to 112%.The correlation between repair effectiveness and the inherent resistance of the RC beams is also elucidated.Furthermore, an analytical expression is proposed for estimating the post-fire load-bearing capacity of reinforced beams, offering a practical and accurate tool for engineering assessments.
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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) | 0.001 | 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) | 0.001 | 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".