Improvement of flammability resistance of epoxy adhesives used in infrastructure applications
Bibliographic record
Abstract
Fire endurance of fibre-reinforced-polymer (FRP) concrete systems is crucial for safe use of FRPs in the construction industry. Nanoclay was introduced into epoxy resin to retard flame spread and improve the fire endurance of bonds between FRP and concrete. Test results show that the addition of nanoclay can greatly improve the flame retardancy of epoxy. With only 2% nanoclay, the limit oxygen index number of epoxy increases by 5 and 10 using two types of mixing methods developed at the Concordia Centre for Composites. The epoxy with the addition of nanoclay possesses self-extinguishing properties, whereas the epoxy without the addition of nanoclay burned completely. After exposure to 260 °C for 2 h, the FRP-concrete system using epoxy and 2% nanoclay adhesive showed 23% greater average residual bonding strength than that using control epoxy adhesive. The nanoclay used in this study is a nontoxic and inexpensive material that may fulfill the requirements for civil engineering applications.Key words: fire resistance, fibre-reinforced polymer, concrete, bond, adhesive, nanoclay, epoxy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".