Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea.
Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea.
Sensors (Basel). 2021 Apr 30;21(9):3118. doi: 10.3390/s21093118.
Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect the adjacent cables and worsen the bridge structure condition. Therefore, we must accurately determine the condition of the cable with a technology-based evaluation strategy. In this paper, we propose a deep learning model that allows us to locate the damaged cable and estimate its cross-sectional area. To obtain the data required for the deep learning training, we use the tension data of the reduced area cable, which are simulated in the Practical Advanced Analysis Program (PAAP), a robust structural analysis program. We represent the sensor data of the damaged cable-stayed bridge as a graph composed of vertices and edges using tension and spatial information of the sensors. We apply the sensor geometry by mapping the tension data to the graph vertices and the connection relationship between sensors to the graph edges. We employ a Graph Neural Network (GNN) to use the graph representation of the sensor data directly. GNN, which has been actively studied recently, can treat graph-structured data with the most advanced performance. We train the GNN framework, the Message Passing Neural Network (MPNN), to perform two tasks to identify damaged cables and estimate the cable areas. We adopt a multi-task learning method for more efficient optimization. We show that the proposed technique achieves high performance with the cable-stayed bridge data generated from PAAP.
斜拉桥会受到自然灾害、天气和车辆荷载等多种因素的影响而受损。特别是斜拉桥的重要且薄弱的构件——斜拉索，如果受损，可能会对相邻的斜拉索产生不利影响，从而恶化桥梁结构状况。因此，我们必须采用基于技术的评估策略来准确确定斜拉索的状况。在本文中，我们提出了一种深度学习模型，该模型可以定位受损的斜拉索并估算其横截面积。为了获得深度学习训练所需的数据，我们使用了在实用先进分析程序（PAAP）中模拟的缩减面积斜拉索的张力数据，PAAP 是一个强大的结构分析程序。我们使用传感器数据的张力和空间信息，将受损斜拉桥的传感器数据表示为一个由顶点和边组成的图。我们通过将张力数据映射到图顶点和传感器之间的连接关系，应用传感器几何形状。我们采用图神经网络（GNN）直接使用传感器数据的图表示。最近备受关注的 GNN 可以用最先进的性能处理图结构数据。我们训练了图神经网络框架——消息传递神经网络（MPNN），以执行识别受损电缆和估算电缆区域的两个任务。我们采用多任务学习方法进行更有效的优化。我们的实验结果表明，所提出的技术在 PAAP 生成的斜拉桥数据上具有很高的性能。
