A Dynamic Bayesian and Machine Learning Framework for Quantitative Evaluation and Prediction of Operator Situation Awareness in Nuclear Power Plants

2026-03-11

AI Summary

This study addresses the limitations of traditional situational awareness (SA) assessment methods—such as SAGAT and SART—which are static and retrospective, thereby failing to capture the dynamic evolution of operators’ cognitive states in nuclear power plants. To overcome this, the authors propose a Dynamic Bayesian Network–Machine Learning–based SA modeling framework (DBML SA) that reconstructs the temporal causal structure of 11 performance-shaping factors from 212 operational event reports, enabling real-time inference and prediction of SA. This framework breaks from the static questionnaire paradigm by supporting dynamic modeling, sensitivity analysis, and early warning capabilities for the first time. Experimental results show that the model predicts SART scores with a mean absolute percentage error of 13.8%, exhibiting no statistically significant difference from subjective assessments (p > 0.05), and identifies training quality and stress dynamics as key drivers of SA degradation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Operator situation awareness is a pivotal yet elusive determinant of human reliability in complex nuclear control environments. Existing assessment methods, such as SAGAT and SART, remain static, retrospective, and detached from the evolving cognitive dynamics that drive operational risk. To overcome these limitations, this study introduces the dynamic Bayesian machine learning framework for situation awareness (DBML SA), a unified approach that fuses probabilistic reasoning and data driven intelligence to achieve quantitative, interpretable, and predictive situation awareness modeling. Leveraging 212 operational event reports (2007 to 2021), the framework reconstructs the causal temporal structure of 11 performance shaping factors across multiple cognitive layers. The Bayesian component enables time evolving inference of situation awareness reliability under uncertainty, while the neural component establishes a nonlinear predictive mapping from PSFs to SART scores, achieving a mean absolute percentage error of 13.8 % with statistical consistency to subjective evaluations (p>0.05). Results highlight training quality and stress dynamics as primary drivers of situation awareness degradation. Overall, DBML SA transcends traditional questionnaire-based assessments by enabling real-time cognitive monitoring, sensitivity analysis, and early-warning prediction, paving the way toward intelligent human machine reliability management in next-generation digital main control rooms.

Problem

• situation awareness
• human reliability
• nuclear power plants
• cognitive dynamics
• operational risk

Innovation

• Dynamic Bayesian Network
• Machine Learning
• Situation Awareness
• Human Reliability
• Performance Shaping Factors