mlsanityData to 7 Oct 2026

Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems

arXiv 2210.06404 ↗First posted 12 Oct 2022cs.LGcited by 4

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Abstract (arXiv)

Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation, and response management procedures related to these systems. Network reliability analysis commonly considers network-level performance and does not consider the more detailed node-level responses due to computational cost. In this paper, we propose a rapid seismic reliability assessment approach for bridge networks based on graph neural networks, where node-level connectivities, between points of interest and other nodes, are evaluated under probabilistic seismic scenarios. Via numerical experiments on transportation systems in California, we demonstrate the accuracy, computational efficiency, and robustness of the proposed approach compared to the Monte Carlo approach.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 65 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

12 of 65 references.

References · 12

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  5. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  6. 2018Graph Convolutional Networks for Text Classificationcited by 345
  7. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  8. 2018Exploring Visual Relationship for Image Captioningcited by 215
  9. 2020Towards Deeper Graph Neural Networks with Differentiable Group Normalizationcited by 113
  10. 2020Reliable Graph Neural Networks via Robust Aggregationcited by 43
  11. 2019Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecastingcited by 15
  12. 2017Deep Learning for Accelerated Reliability Analysis of Infrastructure Networkscited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023End-to-End Heterogeneous Graph Neural Networks for Traffic Assignmentcited by 13
  2. 2025Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networkscited by 4
  3. 2024Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignmentcited by 2
  4. 2025Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazardscited by 2