Degradation characteristics of new bio-resin based-fiber-reinforced polymers for external rehabilitation of structures
Bibliographic record
Abstract
Sustainability has recently become a key issue in the design and manufacture of products, due to dwindling oil reserves and increased environmental awareness. Therefore, bio-sourced resins are suggested to be used as an alternative in order to reduce the environmental impact of composite production. This paper presents the mechanical and physico-chemical characterization and the environmental degradation evaluation of furan resin-based glass fiber reinforced polymer (bio-sourced glass fiber reinforced polymer) for structural retrofitting applications in comparison with those of an equivalent thermoset-based glass fiber reinforced polymer (petro-sourced glass fiber reinforced polymer). Epoxy resin was used as a representative for petroleum-derived synthetic thermoset. To conduct this preliminary study, the following steps were taken: (1) prepare composites made from furan and epoxy resins, (2) characterize the mechanical and physico-chemical properties of the composites, (3) study the moisture absorption, and finally, (4) evaluate the degradation of both composites when subjected to alkaline solutions which was simulated to leached concrete pore solution. The experimental results show that the use of furan resin as polymer matrix in glass fiber reinforced polymer leads to an increase in the moisture absorption and a significant decrease in the degradation in alkaline solution.
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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) | 0.001 | 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".