Modal Identification of Bridges Using Mobile Sensors with Sparse Vibration Data
Modal Identification of Bridges Using Mobile Sensors with Sparse Vibration Data
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DOI：
10.1061/(asce)em.1943-7889.0001733
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发表时间：
2020-04-01
影响因子：
3.3
通讯作者：
Matarazzo, Thomas J.
中科院分区：
文献类型：
--
作者：
Eshkevari, Soheil Sadeghi;Pakzad, Shamim N.;Matarazzo, Thomas J.
动态传感器网络有可能显着提高基础设施监控的速度和规模。结构健康监测（SHM）方法长期以来都是在利用固定传感器网络进行数据采集的前提下发展起来的。在过去的十年中，移动传感器网络在桥梁健康监测中的应用已经出现。然而，在模态识别方面，知识方面仍然存在差距，最终阻碍了在大型结构系统上的实施。本文提出了一种基于移动车辆网络中的传感器的结构模态识别方法：一种已经存在的大规模数据收集机制。车辆传感器网络扫描桥梁在空间和时间上的振动，以构建完整响应的稀疏表示，即低秩的不完整数据矩阵。本文介绍了使用矩阵补全 (MIMC) 方法从大量移动传感器收集的数据中提取动态特性（频率、阻尼和振型）的模态识别。首先使用交替最小二乘法 (ALS) 从稀疏观测值构建稠密矩阵，然后进行分解以进行结构模态识别。本文表明，完整的数据矩阵是空间矩阵和时间矩阵的乘积，可以通过主成分分析（PCA）等方法从中提取模态属性。或者，可以将脉冲响应结构嵌入到时间矩阵中，然后使用牛顿法和逆 Hessian 近似来确定固有频率和阻尼比。对于环境振动的情况，应用自然激励技术（NExT），然后执行结构优化（牛顿法）。这两种方法都进行了数值评估，并在数据稀疏性、模态属性准确性和后处理复杂性方面对结果进行了比较。结果表明，这两种技术都可以提取准确的模态属性，包括从稀疏动态传感器网络数据中提取高分辨率模态形状；他们是第一个使用来自大规模动态传感器网络的数据提供完整模态识别的公司。
Dynamic sensor networks have the potential to significantly increase the speed and scale of infrastructure monitoring. Structural health monitoring (SHM) methods have been long developed under the premise of utilizing fixed sensor networks for data acquisition. Over the past decade, applications of mobile sensor networks have emerged for bridge health monitoring. Yet, when it comes to modal identification, there remain gaps in knowledge that have ultimately prevented implementations on large structural systems. This paper presents a structural modal identification methodology based on sensors in a network of moving vehicles: a large-scale data collection mechanism that is already in place. Vehicular sensor networks scan the bridge's vibrations in space and time to build a sparse representation of the full response, i.e., an incomplete data matrix with a low rank. This paper introduces modal identification using matrix completion (MIMC) methods to extract dynamic properties (frequencies, damping, and mode shapes) from data collected by a large number of mobile sensors. A dense matrix is first constructed from sparse observations using alternating least-square (ALS) then decomposed for structural modal identification. This paper shows that the completed data matrix is the product of a spatial matrix and a temporal matrix from which modal properties can be extracted via methods such as principal component analysis (PCA). Alternatively, an impulse-response structure can be embedded into the temporal matrix and then natural frequencies and damping ratios are determined using Newton's method with an inverse Hessian approximation. For the case of ambient vibrations, the natural excitation technique (NExT) is applied and then structured optimization (Newton's method) is performed. Both approaches are evaluated numerically, and results are compared in terms of data sparsity, modal property accuracy, and postprocessing complexity. Results show that both techniques extract accurate modal properties, including high-resolution mode shapes from sparse dynamic sensor network data; they are the first to provide a complete modal identification using data from a large-scale dynamic sensor network.