Human thermal risk prediction in indoor hyperthermal environments based on random forest. (August 2019)

• Record Type: Journal Article
• Authors: Chong, Daokun; Zhu, Neng; Luo, Wei; Pan, Xiaodi
• Is Part Of: Sustainable cities and society. Volume 49(2019)
• Publication Date: 2019-08
• Subjects: HA heat acclimation -- Tc core temperature -- HR heart rate -- RPE ratings of perceived exertion -- RTS ratings of thermal sensation -- PSI physiological strain index -- PeSI perceptual strain index -- Ta air temperature -- RH relative humidity -- TRI thermal radiation intensity -- CI clothing insulation -- IIPW intensity index of physical work -- LH labor hour -- RF random forest -- WGBT wet bulb globe temperature -- THI temperature humidity index -- ESI environmental stress index -- Tr rectal temperature -- SET safety exposure time -- IG information gain -- TRS thermal risk score -- AUC area under the curve; Hyperthermal environments -- Human thermal risk -- Heat acclimation -- Prediction model -- Random forest; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2019.101595
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: HA can significantly reduce the human thermal risk in hyperthermal environments. Predicting the thermal risk in hyperthermal environments contributes to the safety. Random forest was applied to build the prediction model of human thermal risk. The non-HA and HA model have the high accuracy of 95.15% and 94.01%. The weightings of the six contributing factors to the thermal risk were determined. Abstract: Indoor hyperthermal environments pose a huge threat to the occupants. Predicting workers' thermal risk in indoor hyperthermal environments contributes to the health and safety at work and the control strategies of indoor hyperthermal environments. Moreover, the quantitative effects of heat acclimation (HA) on the thermal risk are unknown and the weightings of the factors influencing thermal risk need to be determined. To these ends, the simulation experiments were conducted in a climate chamber. The subjects were asked to do treadmill exercise to simulate manual labor. During the experiments, core temperature, heart rate, and subjective perception were recorded. Significant differences were found on the physiological strain index and perceptual strain index before and after HA, which indicate that HA could reduce the thermal risk. Air temperature (Ta ), relative humidity (RH), thermal radiation intensity (TRI), clothing insulation (CI), intensity index of physical work (IIPW), and labor hour (LH) were selected as the predictors of the human thermal risk. Given the effect of HA, the non-HA and HA model were built using random forest (RF) with the accuracy of 95.15% and 94.01%, respectively. In addition, the weightings of six contributing factors were determined, and Ta and LH were the most important among them.