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Updated: Jul 13, 2025
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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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Risk coupling analysis under accident scenario evolution: A methodological construct and application
Jianting Yao1,2, Boling Zhang1, Dongdong Wang1
1School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing, China.
Summary
This study introduces a new framework for analyzing coupled risks in dynamic scenarios, focusing on digitization and objective quantification. It helps identify key risk factors and understand accident evolution to prevent cascading failures.
Area of Science:
- Risk analysis
- Systems engineering
- Data mining
Background:
- Dynamic processes often involve interconnected risks, but current analysis methods overlook these coupling effects.
- A need exists for a comprehensive approach to risk coupling that embraces digitization and objective quantification.
Purpose of the Study:
- To propose an integrated framework for analyzing risk coupling in dynamic scenarios.
- To address the limitations of existing studies by incorporating full-process analysis.
Main Methods:
- Utilized the weighted Eclat algorithm for mining risk association rules.
- Employed social network analysis to identify key risk factors.
- Applied stochastic Petri nets for constructing, simulating, and evolving accident scenarios.
Main Results:
- Developed a universal framework for process-oriented analysis of accident scenario evolution.
- Demonstrated the ability to decouple risks by focusing on key factors and breaking accident chains.
- Validated the framework's feasibility and scientific validity through an application to fire risk in Chinese urban communities.
Conclusions:
- The proposed framework offers a novel approach to understanding and managing coupled risks in complex dynamic systems.
- Effective risk decoupling and accident chain interruption are achievable through process-oriented analysis and identification of critical factors.
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