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Title: Wearing comfort of two construction work uniforms
Authors: Chan, APC 
Yang, Y
Wong, KW 
Chan, DWM 
Lam, EWM
Keywords: Artificial neural network
Fuzzy logic
Regression analysis
Clothing comfort
Construction workers
Issue Date: 2015
Publisher: Emerald Group Publishing Limited
Source: Construction innovation, 2015, v. 15, no. 4, p. 473-492 How to cite?
Journal: Construction innovation 
Abstract: Purpose – The aim of this study is to investigate wearing comfort of summer work uniforms judged by construction workers.
Design/methodology/approach – A total of 189 male construction workers participated in a series of wear trials and questionnaire surveys in the summer of 2014. They were asked to randomly wear two types of work uniforms (i.e. uniforms A and B) in the two-day field survey and the subjective attributes of these uniforms were assessed. Three analytical techniques, namely, multiple regression, artificial neural network and fuzzy logic were used to predict wearing comfort affected by the six subjective sensations.
Findings – The results revealed that fuzzy logic was a robust and practical tool for predicting wearing comfort in terms of better prediction performance and more interpretable results than the other models. Pressure attributes were further found to exert a greater effect than thermal–wet attributes on wearing comfort. Overall, the use of uniform B exhibited profound benefits on wearing comfort because it kept workers cooler, drier and more comfortable with less work performance interference than wearing uniform A.
Originality/value – The findings provide a fresh insight into construction workers’ needs for work clothes, which further facilitates the improvement in the clothing tailor-made design and the enhancement of the well-being of workers.
ISSN: 1471-4175
EISSN: 1477-0857
DOI: 10.1108/CI-06-2015-0037
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