Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
This work addresses the need for faster risk assessment in transportation infrastructure for engineers and planners, though it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of rapid seismic reliability assessment for bridge networks by proposing a graph neural network surrogate model, achieving accurate and computationally efficient results compared to Monte Carlo methods in numerical experiments on California transportation systems.
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.