LAUSR

Abstract Hot and humid environments (HAHEs) can jeopardize the health and safety of humans. Accurately evaluating and predicting heat strain contribute to reducing the human thermal risk (HTR). Nevertheless, the traditional evaluation indexes ignore the importance of water-salt metabolism system in resisting heat, and the sweating rate cannot fully reflect heat strain. Furthermore, extant methods of classification and prediction of HTR have defects in objectivity, accuracy, and practicability. To fill these gaps, an experimental study with 24 conditions and 120 person-times was conducted in a climate chamber. During the experiment, the subjects’ oral temperature, heart rate, and dehydration rate were recorded. Based on the experimental data, two principal components with explained variance of 92.773% were extracted by principal component analysis, and with the determination of weightings, a new evaluation index of heat strain in HAHEs was proposed. The HTR was further classified into five levels, from “no risk” to “extremely severe risk” using the K-means cluster. Moreover, a prediction model of the HTR in HAHEs with the goodness of fitting of 0.782 was established using the multiple linear regression, and the weightings of predictors were determined. The results of this study could provide a reference for safety and health at work.