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

Shuai Chen, Huiqiao Jia, Tao Qing, Li Zhang, Xingyu Xiao

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.

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

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.
Paper Structure (27 sections, 6 equations, 9 figures, 9 tables)

This paper contains 27 sections, 6 equations, 9 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Traditional situation awareness evaluation methods
  4. Probabilistic and Data-Driven Models in HRA
  5. Bayesian and Markov Frameworks for Dynamic Probabilistic Modeling
  6. Static Bayesian Network
  7. Markov Model
  8. Dynamic Bayesian Network
  9. Methodology
  10. Overview of the Framework
  11. Data Sources and Preprocessing
  12. Statistical and factor analysis
  13. Dynamic Bayesian Network (DBN) Construction
  14. Experimental Validation
  15. Machine Learning Prediction Module
  16. ...and 12 more sections

Figures (9)

  • Figure 1: Workflow of the proposed Dynamic Bayesian–Machine Learning Framework (DBML-SA) for quantitative evaluation and prediction of operator situation awareness.
  • Figure 2: Causal model of situation awareness factors.
  • Figure 3: Neural network architecture for automatic prediction of SART scores.
  • Figure 4: (a) Computational architecture of the proposed model, illustrating the flow of input features, intermediate processing layers, and probabilistic outputs. (b) Quantitative results of situation awareness (SA) reliability, showing the estimated probabilities and their temporal variations across task phases.
  • Figure 5: Temporal evolution of operator SA reliability inferred by the DBN model.
  • ...and 4 more figures