Performance prediction of flame-retardant clothing using correlations and artificial neural networks: Optimizing firefighter safety

Abstract

Flame-Retardant Clothing serves as a protective shield for firefighters that safeguards them from exposure to heat, flames and other thermal hazards. To achieve an optimal design for the clothing, it is essential to simultaneously account for all the factors affecting the performance of the clothing. The present study employs data from a numerical model to explore heat and moisture transport through clothing subjected to flame exposure. Seventeen non-dimensionless parameters associated with the heat and moisture transport in flame-retardant clothing are obtained. A correlation is developed to link the dimensionless second-degree burn time with other non-dimensional parameters. This correlation provides a means to predict the thermal protective performance (TPP) of the clothing. Additionally, an Artificial Neural Network (ANN) method is employed to determine the TPP of the clothing. Multi-layer feedforward backpropagation networks are utilized to predict the TPP under specified exposure conditions. The findings indicate that both the correlations and the ANN approach adopted in the present study demonstrated promising results. However, the ANN model predictions show better agreement with model data in comparison to the results derived from the developed correlation. The maximum percentage error in the predicted non-dimensional second degree burn time using ANN is limited to 10 %.