Bridges are susceptible to deterioration and damage as they age and should be routinely assessed to evaluate their integrity and safety for service. Traditionally, structural monitoring has comprised visual inspections, however this is both time and labor intensive. Researchers have shown that sensors on moving vehicles may provide insight into the dynamic behavior of bridges. Accelerometers within smartphones may serve as the sensors from which data is collected; thus, enabling massive data collection from a fleet of potential monitoring vehicles. This paper presents four postprocessing strategies for estimating bridge frequencies from smartphone acceleration data streams with no a priori information about the mass or stiffness of the bridge or vehicle. These techniques utilize the DFT and MUSIC algorithms to calculate vehicle acceleration frequency spectrums from which the fundamental bridge vibration frequency may be estimated. Both single-vehicle and crowdsourced postprocessing techniques are investigated. Utilizing the MUSIC algorithm within a crowdsourcing framework, the correct bridge frequency was identified in all analytical simulations within 4% error, representing a significant increase in performance over single-vehicle estimations made using MUSIC. The effect of user interaction with the smartphone is studied by including superimposed acceleration signals on 25–100% of analytical results; the superimposed user events included a dropped smartphone and talking on a smartphone. Increasing the percentage of noisy signals in the pool of evaluated accelerations generally reduces performance with the exception of crowdsourced estimations made using the MUSIC algorithm, which proved to be robust against user interaction with the smartphone.

中文翻译：

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使用智能手机的桥频率估计策略

桥梁随着时间的流逝易于老化和损坏，应定期评估以评估其完整性和服务安全性。传统上，结构监视包括目视检查，但是这既费时又费力。研究人员已经表明，行驶中的车辆上的传感器可以洞悉桥梁的动态行为。智能手机中的加速度计可以用作收集数据的传感器。因此，可以从一组潜在的监控车辆中收集大量数据。本文提出了四种后处理策略，用于从智能手机加速度数据流估计桥频率，而没有关于桥或车辆的质量或刚度的先验信息。这些技术利用DFT和MUSIC算法来计算车辆加速度频谱，从中可以估算出桥梁的基本振动频率。研究了单车和众包后处理技术。在众包框架内利用MUSIC算法，在所有分析模拟中都可以识别出正确的桥梁频率，误差不超过4％，这表明与使用MUSIC进行的单车估计相比，性能有了显着提高。通过在25％至100％的分析结果中包括叠加的加速度信号来研究用户与智能手机交互的影响；叠加的用户事件包括智能手机掉落和智能手机通话。