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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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A novel algorithm for model uncertainty reduction in trapezoidal fuzzy fault tree risk assessment
Yuanyuan Zhang1, Long Zhao1, Tao Zhang2
1School of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, Liaoning, China.
Plos One
|December 15, 2025
Summary
This study introduces a new algorithm for trapezoidal fuzzy fault tree analysis, significantly reducing model uncertainty in risk assessments. The method enhances reliability for complex systems in industries like nuclear energy and chemical processing.
Area of Science:
- Engineering
- Computer Science
- Risk Management
Background:
- Complex systems risk assessment relies on fuzzy fault trees.
- Conventional methods face challenges in probability calculation and model uncertainty, impacting reliability.
Purpose of the Study:
- To propose a novel algorithm for reducing model uncertainty in trapezoidal fuzzy fault tree analysis.
- To enhance the accuracy and reliability of risk evaluations in complex systems.
Main Methods:
- Developed a new algorithm leveraging the cut-set theorem for trapezoidal fuzzy fault tree analysis.
- Operated directly on trapezoidal fuzzy numbers without defuzzification, preserving fuzzy information.
- Accommodated OR-only, AND-only, and mixed OR/AND gate logics.
Main Results:
- Achieved a 45.25% reduction in model uncertainty, outperforming existing methods (36.65%).
- Demonstrated 99.40% consistency with benchmark literature, confirming accuracy.
- Showcased exceptional stability with <1% output variation under ±15% input perturbations.
Conclusions:
- The novel algorithm offers a reliable and scalable solution for risk assessment in high-stakes industries.
- The method effectively reduces model uncertainty and enhances accuracy in fuzzy fault tree analysis.
- Proven robustness and stability make it suitable for real-world applications with data uncertainties.
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