Using Artificial Neural Network Modeling to Analyze the Thermal Protective and Thermo-Physiological Comfort Performance of Textile Fabrics Used in Oilfield Workers' Clothing.
Int J Environ Res Public Health
; 18(13)2021 06 30.
Article
in En
| MEDLINE
| ID: mdl-34208824
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Heat Stress Disorders
/
Oil and Gas Fields
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Int J Environ Res Public Health
Year:
2021
Document type:
Article
Affiliation country: