A Machine Learning-Based Risk Assessment Study of Unsafe Behavior of Workers in Nuclear Power Plants Under Construction

Most accidents during the construction of nuclear power plants are caused by human unsafe behavior. How to scientifically determine the risk management priority of human unsafe behaviors is the basis for effectively preventing accidents in under-construction nuclear power plants. Although employees are adopted for control in under-construction nuclear power plants, the records of unsafe behaviors are mostly recorded by inspectors, and the records of behaviors may have missing values. To overcome the above problems, this paper applies machine learning algorithms to construct an employee behavioral risk assessment model. Firstly, by analyzing the influencing factors of unsafe behaviors, the assessment indexes are proposed, then the Random Forest algorithm is used to obtain the characteristic importance of the proposed indexes and exclude those with smaller characteristic importance. Finally, the harmony search (HS) algorithm is used to optimize the back propagation (BP) neural network to construct an assessment model and compare with the BP evaluation model. The results show that the HS-BP model is more accurate and efficient. The results show that the method can comprehensively and effectively analyze workers‘ unsafe behaviors, and the BP neural network is optimized to construct the assessment model using the Harmonic Search algorithm, which is more accurate than the original model. The use of the machine learning method to assess workers’ behaviors can objectively output the risk level and overcome the one-sidedness and subjectivity of the traditional expert evaluation method.