Impact of Radiative Exposures on the Mechanical Properties of Fire-Resistant Fabrics
Bibliographic record
Abstract
Firefighters’ protective garments are designed to protect them from elevated temperature and heat flux environments. Typically, these garments are made of three layers: an outer shell, a moisture barrier, and a thermal liner. At present, new firefighters’ garments must meet performance criteria specified in test standards, but these do not apply to in-use garments. As the performance of this clothing may degrade with use, quantitative methods for determining the useful life of these garments without destroying them is a need for the fire service. One area of such research has been the development of correlations between near infrared (NIR) spectral results and changes in fabric properties. Research at the University of Saskatchewan (U of S) has shown that NIR measurements can be correlated to deterioration in mechanical strength after thermal ageing. This study examined the performance of two Kevlar®/PBI fabrics after exposures to heat fluxes ranging from 10 to 70 kW/m2 for durations ranging from 15 to 1200 s. These exposures were conducted using a cone calorimeter and have been selected as representative of the wide range of conditions expected on the fireground. After examination using an NIR spectrometer, tensile testing was conducted on the specimens. The tensile strength values for thermally aged fabrics were then compared against criteria in standards for new clothing. Three correlations between exposure and duration were developed based on multi-variable linear regressions, multi-variable nonlinear regressions, and single-variable nonlinear regressions. These correlations were constructed from this research and past research datasets. These correlations aim to predict the degradation these types of fabrics experience, and could be used in future degradation. It was found that the multi-variable nonlinear regression correlation was the most successful across different exposures, while the single-variable nonlinear regression was able to predict degradation more accurately to an average standard error of 4%.
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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 | 0.001 |
| 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) | 0.002 | 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".