Modeling for predicting the thermal protective and thermo-physiological comfort performance of fabrics used in firefighters' clothing

Standardized test methods are available for measuring the thermal protective as well as thermo-physiological comfort Performance of fabrics used in firefighters' clothing. However, these tests are usually fabric destructive in nature, time consuming, and/or expensive to carry out on a regular basis. Hence, the availability of empirical models could be useful for conveniently predicting the thermal protective and thermo-physiological comfort performances from the fabric properties. The aim of this study is to develop individual models for predicting thermal protective and thermo-physiological comfort performances of fabrics. For this, different single- and multi-layered fabrics that are commercially used to manufacture firefighters' protective clothing were selected, and the fundamental properties of these fabrics (weight, thickness, thermal resistance, air-permeability, evaporative resistance, and water spreading speed) were measured using the standard test methods developed by the International Organization for Standardization (ISO) or the American Association of Textile Chemists and Colorists. The thermal protective performance of these fabrics was measured by the ISO 9151:2016 test method under 80 kW/m ² flame exposure. The thermo-physiological comfort performance of fabrics was determined by the ISO 18640-1:2018 test method and a statistical model. Thereafter, the key fabric properties affecting the thermal protective and thermo-physiological comfort performances of fabrics were determined statistically. It has been found that thermal and evaporative resistances are the key fabric properties to affect the thermal protective performance, whereas the fabric weight, evaporative resistance, and water spreading speed are the key properties to affect the thermo-physiological comfort performance. By employing these key fabric properties, Multiple Linear Regression and Artificial Neural Network (ANN) models were developed for predicting the thermal protective and thermo-physiological comfort performances. Through a comparison of the predicting performance parameters of these models, it has been found that ANN models can more accurately predict the performances of fabrics. These models can be implemented in the textile industry and academia for effectively and conveniently predicting the thermal protective and thermo-physiological comfort performances only by utilizing the key fabric properties.