论文标题
图形神经网络代理用于高速公路桥系统的地震可靠性分析
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
论文作者
论文摘要
运输网络的快速可靠性评估可以增强与这些系统有关的准备，降低风险和响应管理程序。网络可靠性分析通常考虑网络级的性能，并且由于计算成本而不考虑更详细的节点级响应。在本文中，我们提出了基于图神经网络的桥接网络的快速地震可靠性评估方法，在概率地震场景下，对节点级连接（在兴趣点和其他节点之间）进行了评估。通过在加利福尼亚州的运输系统上进行的数值实验，我们证明了与蒙特卡洛方法相比，提出方法的准确性，计算效率和鲁棒性。
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.
