Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

中文翻译：

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使用移动传感数据和传统系统识别技术进行桥梁监测


移动传感已成为空间密集的固定传感器网络的经济上可行的替代方案，它利用了当今广泛使用的智能手机的众包数据。最近，现场实验表明，使用异步众包移动传感数据，可以估计电桥模态频率和绝对振型（振型的绝对值，即没有相位信息的振型）。然而，时间同步数据和改进的系统识别技术对于在同一上下文中估计频率、全振型和阻尼比是必要的。本文提出了一个框架，该框架仅使用两个时间同步移动传感器来估计空间密集的频率响应矩阵。随后，该矩阵可以集成到现有的系统识别方法和结构健康监测平台中，包括自然激励技术本征系统实现算法和频域分解。该方法对大跨度桥梁进行了数值测试，并使用了实验室规模的实验。在实验室规模的实验中，手推车上的同步智能手机穿过一座模型桥。用两种系统识别方法分析所得的交叉谱，并证明了所提出的框架的有效性，对前六种模式（包括垂直和扭转）产生了高精度 （模态保证标准值高于 0.94）。这个新颖的框架将移动传感的监控可扩展性与用户对传统系统识别技术的熟悉程度相结合。