The performance of FRP-strengthened concrete slabs in fire
Bibliographic record
Abstract
Continuing advances in the manufacturing techniques and performance of fibre-reinforced polymer (FRP) materials have allowed FRPs to move from playing a secondary role in civil infrastructure to one that is a genuinely feasible construction alternative. With a wider database of knowledge and established methods for prediction of fire endurance, applications of FRP materials can expand into interior building applications, where fire is a critical concern. An on-going research program at Queen's University and the National Research Council of Canada (NRC) aims to evaluate the behaviour of FRP-strengthened members at high temperature, and to recommend design guidelines for safe use of FRP in buildings. Experimental studies were conducted to evaluate the heat transfer behaviour of small-scale concrete slabs strengthened with FRP sheets and insulated with a unique two-component fire protection system. The slabs were subjected to a standard ASTM E119 fire and their overall behaviour was observed. The thickness of the insulation was previously established as a parameter vital to the fire endurance of FRP-strengthened reinforced concrete members. Thus, the insulation thickness was varied to study its ability to maintain low temperatures at the FRP bondline, which are critical to preventing delamination of FRP and insulation. A finite difference model was developed to predict temperatures throughout the insulation, FRP and concrete during fire exposure. Model predictions were compared against test data in an effort to validate the model. Both experimental and analytical data are promising in that they indicate that appropriately insulated FRP-strengthened concrete slabs can provide adequate fire performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".