Owing to the presence of many unforeseen hazards, underground coal mining happens to be one of the most accident-prone occupations (Cho and Lee 1978). There is every possibility of occurrence of life-threatening events in underground mines, like roof strata collapse, outburst of toxic and inflammable gases, firedamp and coal dust explosions, sudden inrush of water, etc. (Ma et al. 2016; Wang et al. 2018; Dursun 2020). Presence of high heat, humidity and dust in underground mines makes the mine environment more complex and uncomfortable for the miners (Roy et al. 2021). Despite presence of numerous hazards, underground coal mining could not be scaled down due to high energy demand across the globe. Coal meets 60% and 54% of the total energy demand of two leading coal producing countries of the world, China and India, respectively (Gui et al. 2020; Ministry of power Government of India 2020). Due to rising demand of coal, the mine operators are compelled to mine shallow coal deposits with high-capacity machines, which results speedy depletion of easily minable deposits and necessitates mining of deep-seated coal deposits. As underground coal mines are nowadays becoming deep, extended over large area and mined with high-capacity machines, the heat addition from various sources like geothermal gradient, auto-compression of air and heat ejection from machinery becomes high. Therefore, there is prevalence of heat stress in underground coal mine environment.
Heat stress has detrimental effects on the health safety and productivity of underground coal miners. The core body temperature of humans is maintained close to 37°C, which may rise during working in heat stress environment (Lemke and Kjellstrom 2012). In such condition, the central nervous system of workers may get destabilized which may be reflected by the symptom of mental retardation, lack of concentration, and decreased ability to work (Nie et al. 2018). When the core body temperature reaches 41°C, then there is a higher chance of occurrence of heat stroke that may result death of miner if not treated on time (Brake and Bates 2002a; ACGIH 2019). Working in high heat zone can cause malfunctioning of nerve and muscle, loss of concentration, which ultimately result in productivity loss and increased rate of heat related accidents (Zhao et al. 2009). Heat stress dominated underground environment causes decrease in productivity and increase in safety and health related issues among miners (Xiaojie et al. 2011; Yi et al. 2019). It has been studied that the human nervous system has a propensity to limit the body activity and the productivity of miners is reduced in heat stress environment (Kocsis and Sunkpal 2017b).
It has been found through a systematic literature review that researchers have proposed several indices to assess the intensity of heat stress in an underground mine environment (Roy et al. 2022). However, a heat stress index that is globally accepted and applicable in all environmental could not be developed yet (Brake and Bates 2002b; Roghanchi et al. 2015). It is also realised that very little effort has been given in predicting heat stress in underground coal mine working places. Considering this as a major research gap, this study focused on the development of an efficient but simple heat stress prediction model for underground coal mines. Prediction of heat stress in underground mine environment will help the mine operators in taking precautionary measures for controlling heat stress and enhancing the health, safety and productivity of the miner.
In this study, an extensive field survey has been conducted to record several mine environmental parameters along with heat stress. Genetic Programming (GP) is done by taking survey data as input to develop a simple relationship between the most sensitive environmental parameters and heat stress. GP is an Artificial Intelligence tool developed by Koza in the year of 1992. The idea of GP came from the Darwinian principle of survival and reproduction of the fittest (Koza 1992). GP has been widely applied in the field of engineering over the last two decades (Hosseini and Nemati 2015). In earth science engineering, GP has been used for prediction of back break in opencast mines (Shirani Faradonbeh et al. 2016; Sharma et al. 2021), fly-rock assessment due to blasting (Faradonbeh et al. 2016), prediction of subsidence due to underground mining (Li et al. 2007), assessing the strength of intact rocks (Asadi et al. 2011), evaluation of deformation modulus of rock mass (Beiki et al. 2010), prediction of tensile and compressive strength of limestone (Baykasoǧlu et al. 2008), etc. As far as the knowledge of the authors is concerned, GP has not yet been applied for heat stress prediction in underground coal mines. Hence, authors have realized its wide, versatile and successful application in geosciences, and probably this study is first of its kind that apply GP in the field of heat stress prediction in underground coal mines.