Investigating construction workers' perception of risk, likelihood, and severity using electroencephalogram and machine learning

Understanding how workers perceive risk is essential to construction safety management. Firstly, an event-related potential (ERP) experiment was conducted to investigate the relationship between risk, likelihood, and severity. Then, a linear model was developed to predict workers' risk perception based on ERP components and quantify the relative importance of severity to likelihood. Finally, an additive model was constructed to reflect the risk perception pattern. The results indicate: (1) Workers' emotional responses stem from the process of associating accident consequences in severity assessment, which is represented by the late positive potential (LPP) component. (2) Workers' risk perception relies more on severity compared with likelihood. (3) The additive model (risk = 0.203 * likelihood +0.758 * severity) better matches the risk perception patterns than the multiplicative model. The research results provide a new perspective for understanding workers' risk perception patterns and contributing to proactive safety management in the construction industry. [Display omitted] •Construction workers assess risksconsidering both likelihood and severity, with severity playing a more critical role.•Emotional response in risk perception stems from associating the consequences of accidents during severity assessment.•EEG signals and machine learning can quantify the relative importance of likelihood and severity in risk perception.•The additive model better reflects workers' risk perception pattern than the multiplicative model.