Climate-change adaptation to extreme heat on construction sites: A wearable-free, simplified machine-learning model for predicting workers’ heat strain

Construction workers are increasingly vulnerable to heat strain due to prolonged outdoor exposure and the intensifying effects of global warming. To support climate-adaptation strategies at job sites, this study developed a wearable-free, simplified machine-learning (ML) model capable of delivering real-time, individualized heat strain forecasts. A living-lab study was conducted at two construction sites in South Korea, collecting 95,340 records from 67 workers. Even under similar climate conditions, heat strain varied significantly among individuals, reflecting the combined influence of personal attributes and occupational demands.