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\nheat flux environments. Typically, these garments are made of three layers: an outer shell, a\nmoisture barrier, and a thermal liner. At present, new firefighters’ garments must meet\nperformance criteria specified in test standards, but these do not apply to in-use garments. As the\nperformance of this clothing may degrade with use, quantitative methods for determining the\nuseful life of these garments without destroying them is a need for the fire service. One area of\nsuch research has been the development of correlations between near infrared (NIR) spectral\nresults and changes in fabric properties. Research at the University of Saskatchewan (U of S) has\nshown that NIR measurements can be correlated to deterioration in mechanical strength after\nthermal ageing.\nThis study examined the performance of two Kevlar®/PBI fabrics after exposures to heat fluxes\nranging from 10 to 70 kW/m2 for durations ranging from 15 to 1200 s. These exposures were\nconducted using a cone calorimeter and have been selected as representative of the wide range of\nconditions expected on the fireground. After examination using an NIR spectrometer, tensile\ntesting was conducted on the specimens. The tensile strength values for thermally aged fabrics\nwere then compared against criteria in standards for new clothing.\nThree correlations between exposure and duration were developed based on multi-variable linear\nregressions, multi-variable nonlinear regressions, and single-variable nonlinear regressions.\nThese correlations were constructed from this research and past research datasets. These\ncorrelations aim to predict the degradation these types of fabrics experience, and could be used in\nfuture degradation. It was found that the multi-variable nonlinear regression correlation was the\nmost successful across different exposures, while the single-variable nonlinear regression was\nable to predict degradation more accurately to an average standard error of 4%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".