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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.06404 |
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| _version_ | 1866916366609547264 |
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| author | Liu, Tong Meidani, Hadi |
| author_facet | Liu, Tong Meidani, Hadi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_06404 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems Liu, Tong Meidani, Hadi Machine Learning 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. |
| title | Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2210.06404 |