Repair of Fire-Damaged Circular and Square Columns Using CFRP Composites: A Comprehensive Review
Bibliographic record
Abstract
In the field of structural engineering, the repair and rehabilitation of fire-damaged circular and square columns pose significant challenges. Carbon Fiber Reinforced Polymer (CFRP) composites have gained popularity as a potentially effective method for restoring the structural integrity of damaged structural members. The purpose of this investigation is to provide a state-of-the-art review of existing experimental and numerical studies in the literature on repairing fire-damaged square/circular columns using CFRP composites. The effectiveness of CFRP as a repairing technique in restoring the axial load-carrying capacity and stiffness of fire-damaged circular, square, and rectangular columns is evaluated. Based on past investigations, it has been concluded that CFRP composites can restore 70-169.2% of the axial capacity and 0-29.2% of the lost resistance. Moreover, the review offers a critical assessment of previous experimental and numerical investigations, highlighting their significant contributions to the advancement of knowledge regarding CFRP-based column repair. A comparative analysis of the experimental results from available studies is carried out, accompanied by a concise examination of the gaps, and a pathway for future researchers is also suggested. In addition to this, probable challenges and limitations, such as stiffness loss after repairing fire-damaged columns with CFRP composites, and ways to counter them, are also summarized in this study. The article provides engineers, researchers, and practitioners involved in the restoration and repair of fire-damaged circular and square columns using CFRP composites with insightful recommendations derived from an exhaustive synthesis of prior literature and case studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".