Most of the fatalities and injuries of oilfield workers result from inadequate protection and comfort by their clothing under various work hazards and ambient environments. Both the thermal protective performance and thermo-physiological comfort performance of textile fabrics used in clothing significantly contribute to the mitigation of workers’ skin burns and heat-stress-related deaths. This study aimed to apply the ANN modeling approach to analyze clothing performance considering the wearers’ sweat moisture and the microclimate air gap that is generated in between their body and clothing. Firstly, thermal protective and thermo-physiological comfort performance of fire protective textiles used in oilfield workers’ clothing were characterized. Different fabric properties (e.g., thickness, weight, fabric count), thermal protective performance, and thermo-physiological comfort performance were measured. The key fabric property that affects thermal protective and thermo-physiological performance was identified as thickness by statistical analysis. The ANN modeling approach could be successfully implemented to analyze the performance of fabrics in order to predict the performance more conveniently based on the fabric properties. It is expected that the developed models could inform on-duty oilfield workers about protective and thermo-physiological comfort performance and provide them with occupational health and safety.

This study aims to characterize and model the thermal protective fabrics usually used in workwear under Molotov cocktail exposure. Physical properties of the fabrics were measured; and, thermal protective performances of the fabrics were evaluated under a fire exposure generated from the laboratory-simulated Molotov cocktail. The performance was calculated in terms of the amount of thermal energy transmitted through the fabrics; additionally, the time required to generate a second-degree burn on wearers’ bodies was predicted from the calculated transmitted thermal energy. For the characterization, the parameters that affected the protective performance were identified and discussed with regards to the theory of heat and mass transfer. The relationships between the properties of the fabric systems and the protective performances were statistically analyzed. The significant fabric properties affecting the performance were further employed in the empirical modeling techniques − Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for predicting the protective performance. The Coefficient of Determination (R²) and Root Mean Square Error (RMSE) of the developed MLR and ANN models were also compared to identify the best-fit model for predicting the protective performance. This study found that thermal resistance and evaporative resistance are two significant properties (P-Values < 0.05) that negatively affect the transmitted thermal energy through the fabric systems. Also, R² and RMSE values of ANN model were much higher (R² = 0.94) and lower (RMSE = 37.42), respectively, than MLR model (R² = 0.73; RMSE = 191.38); therefore, ANN is the best-fit model to predict the protective performance. In summary, this study could build an in-depth understanding of the parameters that can affect the protective performance of fabrics used in the workwear of high-risk sectors employees and would provide them better occupational health and safety.

This study aims to investigate hot water immersion with compression protective performance of textile fabrics used in firefighters’ clothing. This study has two key objectives – firstly, to characterize the protective performance of fabrics under different types of hot water immersion with compression exposures; secondly, to empirically analyze the protective performance of these fabrics under different exposures. To accomplish both the objectives, the physical properties (e.g., thickness, air permeability) of multi-layered fabrics that are commonly used in firefighters’ clothing were measured. Next, the protective performances of these fabrics were evaluated under different exposures. The experimental data obtained were statistically analyzed to identify the effects of the fabrics’ physical properties on the performance. Also, the performances provided by the fabrics were compared, and the nature of heat and mass transfer through the fabrics was explored. Using the significant fabric properties that affected the performance, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) modeling techniques were used to empirically predict the performance of the fabrics. The best prediction models were then employed for saliency testing to understand the relative importance of the significant fabric properties on the performance. The study demonstrates that the protective performance of textile fabrics varies with the exposures, depending upon the mass transfer through fabrics. In these exposures, fabric thickness, air or water-vapor permeability, and evaporative resistance are found to be the primary properties to consider in protecting the wearer. In this study, it has been identified that ANN models can be effectively used in comparison to MLR models for predicting the protective performance. By analyzing the best-fit ANN models, it is identified that different fabric properties play a key role in predicting the protective performance. Overall, this study would enhance the understanding of fabric materials used in firefighters’ clothing. This deeper understanding could be applied to engineer new test standards and fabric materials for clothing to provide optimum occupational health and safety for firefighters.