Postfire Estimation of Heating Temperatures Experienced by Fire Retardant Coatings Using Smartphone Videos and Machine Learning

Abstract

Accurate estimation of heating temperatures experienced by fire retardant coatings (FRCs) is crucial in identifying the ignition source during fire investigations. While traditional methods, such as spectroscopy, effectively capture the compositional changes in FRC at various heating temperatures, they are typically bulky, costly, and unsuitable for rapid field analysis. This study proposes the use of smartphone and machine learning to predict the heating temperatures of FRC. A smartphone is employed to capture short videos of FRC samples illuminated by its color-changing screen. Video frames are then decomposed into color images and converted into spectral data for further processing. Linear and nonlinear regression models are applied to identify key variables and enhance predictive accuracy. The performance of smartphone-based temperature estimation is compared to that of hyperspectral imaging and laser-induced breakdown spectroscopy. In the test phase, the coefficient of determination for smartphone-based estimation ranges from 0.946 to 0.962, often surpassing that of benchmark methods. These results indicate that smartphones can provide a low-cost, effective means for estimating heating temperatures of FRC in fire investigations.