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Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
https://orcid.org/0000-0002-3667-917X
https://orcid.org/0000-0003-4651-2696
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.
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
https://orcid.org/0000-0002-3667-917X
https://orcid.org/0000-0003-4651-2696
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.
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
https://orcid.org/0000-0002-3667-917X
https://orcid.org/0000-0003-4651-2696
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.
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
J. Infrastruct. Syst.
Liu, Tong (author) / Meidani, Hadi (author)
2024-12-01
Article (Journal)
Electronic Resource
English
Seismic reliability analysis for highway network system
| British Library Conference Proceedings | 2001
Seismic Reliability of Highway Transportation Systems
| ASCE | 2018
Reliability Analysis of a Highway Bridge
| British Library Conference Proceedings | 1993
Performance-Based Reliability Assessment and Calibration for Seismic Highway Bridge Design
| British Library Conference Proceedings | 1999