A quantitative method to compare the effect of thermal aging on the mechanical performance of fire protective fabrics
Bibliographic record
Abstract
ABSTRACT A method has been proposed to provide a means to compare in a quantitative and comprehensive way the mechanical performance of fire protective fabrics under long‐term thermal exposure. These high‐performance materials experience a reduction in their performance overtime due to the various conditions they are exposed to during the lifetime of the clothing. The proposed method consists in a system of two equations fitting the time–temperature‐performance data: the Arrhenius model combined with the time–temperature superposition principle, and the three‐parameter Hill equation. The result of the data analysis using this method is provided in terms of four parameters: the temperature effect, the time rate, the degradation midpoint time, and the ultimate strength. It was used to compare the effect of accelerated thermal aging on the tear strength of seven different fabrics used in fire protective clothing. In all cases, a very good agreement was observed with both the Arrhenius model and the Hill equation. However, none of the fabrics studied appeared to stand as displaying all the characteristics that would be ideal for long‐term fire protection. The best solution is thus a compromise that will depend on the type of activity conducted and the type of conditions experienced. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47045.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".