Quantifying extreme failure scenarios in transportation systems with graph learning
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View abstract on PubMed
Summary
This summary is machine-generated.We developed a graph learning method (GAE-IS) to efficiently estimate extreme event probabilities in complex engineering systems. This approach significantly reduces computational costs and improves accuracy for large-scale network reliability assessments.
Area Of Science
- Engineering
- Computer Science
- Statistics
Background
- Assessing extreme events in complex engineering systems is crucial for reliability and resilience.
- Estimating failure probabilities is computationally expensive due to event rarity and system complexity.
- Traditional importance sampling methods face challenges with high costs in large-scale systems.
Purpose Of The Study
- To propose a novel graph learning approach for efficient statistical analysis of extreme events.
- To reduce the computational burden associated with estimating rare event probabilities in complex networks.
- To enhance the accuracy and efficiency of reliability and resilience assessments.
Main Methods
- Introduced a graph learning approach: importance sampling based on graph autoencoder (GAE-IS).
- Integrated a modified graph autoencoder (criticality assessor) with cross-entropy-based importance sampling.
- Decoupled component criticality from vulnerability in the analysis workflow.
Main Results
- GAE-IS demonstrated notable transferability across different network types.
- Achieved significant reductions in computational costs for importance sampling (one to two orders of magnitude).
- Provided more accurate probability estimations for extreme failures in large-scale networks.
Conclusions
- GAE-IS offers a computationally efficient and accurate method for extreme event probability estimation.
- The approach is highly effective for large-scale engineering systems, particularly road networks.
- This methodology advances system design, reliability, and resilience assessment.
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