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Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing

Author

Listed:
  • Sumit Mandal

    (Department of Design, Housing and Merchandising, Oklahoma State University, Stillwater, OK 74078-5061, USA)

  • Nur-Us-Shafa Mazumder

    (Department of Design, Housing and Merchandising, Oklahoma State University, Stillwater, OK 74078-5061, USA)

  • Robert J. Agnew

    (Fire Protection and Safety Engineering and Technology Program, Oklahoma State University, Stillwater, OK 74078-5061, USA)

  • Indu Bala Grover

    (Department of Computer Engineering, YMCA Institute of Engineering, Faridabad 121006, India)

  • Guowen Song

    (Department of Apparel, Events, and Hospitality Management, Iowa State University, Ames, IA 50011-2100, USA)

  • Rui Li

    (Department of Apparel, Events, and Hospitality Management, Iowa State University, Ames, IA 50011-2100, USA)

Abstract

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.

Suggested Citation

  • Sumit Mandal & Nur-Us-Shafa Mazumder & Robert J. Agnew & Indu Bala Grover & Guowen Song & Rui Li, 2021. "Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers’ Clothing," IJERPH, MDPI, vol. 18(13), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:13:p:6991-:d:585330
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    Cited by:

    1. Mauricio A. Ramírez-Moreno & Patricio Carrillo-Tijerina & Milton Osiel Candela-Leal & Myriam Alanis-Espinosa & Juan Carlos Tudón-Martínez & Armando Roman-Flores & Ricardo A. Ramírez-Mendoza & Jorge de, 2021. "Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace—A Pilot Study," IJERPH, MDPI, vol. 18(22), pages 1-20, November.

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