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Updated: Oct 7, 2025
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Laser-heating and Radiance Spectrometry for the Study of Nuclear Materials in Conditions Simulating a Nuclear Power Plant Accident
Published on: December 14, 2017
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Application of a Bayesian network to quantify human reliability in nuclear power plants based on the SPAR-H method
Shengyuan Yan1, Kai Yao1, Fengjiao Li1
1College of Mechanical and Electrical Engineering, Harbin Engineering University, China.
Summary
This study introduces a new method to quantify human reliability in nuclear power plants (NPPs) using the SPAR-H framework. The approach effectively reduces human error by integrating fuzzy logic and Bayesian reasoning for more accurate risk assessment.
Area of Science:
- Nuclear Engineering
- Risk Assessment
- Human Factors
Background:
- Human error is a significant contributor to nuclear power plant (NPP) accidents.
- Human Reliability Analysis (HRA) is crucial for mitigating these errors.
Purpose of the Study:
- To develop and validate a novel method for quantifying human reliability in NPPs.
- To enhance the accuracy of risk assessment by addressing human error factors.
Main Methods:
- The study adapted the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method.
- Fuzzy logic (triangular fuzzy numbers and IF-THEN rules) was employed to quantify qualitative data.
- Bayesian reasoning was utilized to integrate data and calculate human reliability.
Main Results:
- The developed method successfully quantifies human reliability within the NPP context.
- Results obtained were consistent with those from the Cognitive Reliability and Error Analysis Methods (CREAM).
Conclusions:
- The proposed method offers a robust tool for quantifying human reliability in nuclear power systems.
- This approach can aid in reducing human error and improving overall plant safety.
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