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/m2 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.
Fabric systems used in firefighters' thermal protective clothing should offer optimal thermal protective and thermo-physiological comfort performances. However, fabric systems that have very high thermal protective performance have very low thermo-physiological comfort performance. As these performances are inversely related, a categorization tool based on these two performances can help to find the best balance between them. Thus, this study is aimed at developing a tool for categorizing fabric systems used in protective clothing. For this, a set of commercially available fabric systems were evaluated and categorized. The thermal protective and thermo-physiological comfort performances were measured by standard tests and indexed into a normalized scale between 0 (low performance) and 1 (high performance). The indices dataset was first divided into three clusters by using the k-means algorithm. Here, each cluster had a centroid representing a typical Thermal Protective Performance Index (TPPI) value and a typical Thermo-physiological Comfort Performance Index (TCPI) value. By using the ISO 11612:2015 and EN 469:2014 guidelines related to the TPPI requirements, the clustered fabric systems were divided into two groups: Group 1 (high thermal protective performance-based fabric systems) and Group 2 (low thermal protective performance-based fabric systems). The fabric systems in each of these TPPI groups were further categorized based on the typical TCPI values obtained from the k-means clustering algorithm. In this study, these categorized fabric systems showed either high or low thermal protective performance with low, medium, or high thermo-physiological comfort performance. Finally, a tool for using these categorized fabric systems was prepared and presented graphically. The allocations of the fabric systems within the categorization tool have been verified based on their properties (e.g., thermal resistance, weight, evaporative resistance) and construction parameters (e.g., woven, nonwoven, layers), which significantly affect the performance. In this way, we identified key characteristics among the categorized fabric systems which can be used to upgrade or develop high-performance fabric systems. Overall, the categorization tool developed in this study could help clothing manufacturers or textile engineers select and/or develop appropriate fabric systems with maximum thermal protective performance and thermo-physiological comfort performance. Thermal protective clothing manufactured using this type of newly developed fabric system could provide better occupational health and safety for firefighters.
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