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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Quantifying extreme failure scenarios in transportation systems with graph learning.

Mingxue Guo1, Tingting Zhao1, Jianxi Gao2

  • 1School of Systems Science, Beijing Jiaotong University, Beijing 100044, China.

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Summary

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.

Keywords:
extreme failuregraph autoencoderimportance samplingroad networksampling efficiency

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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.