基于异步移动传感数据的桥梁结构健康监测
Bridge Structural Health Monitoring using Asynchronous Mobile Sensing Data
摘要：
这项研究提出了一种灵活的方法，桥梁模态识别使用智能手机收集的数据，通过大量的车辆。随着移动传感器的每次行程，对桥梁的时空响应进行采样，加上各种噪声源，例如，车辆动力学、环境影响和道路轮廓。本文提供了进一步的证据来支持这一假设，通过行程聚合，可以减轻这种噪声的影响，并显示出真正的桥梁动力学。在本研究中，连续小波变换被应用到每个行程，并结合结果来估计桥梁的结构模态响应。提出了一种基于连续小波的众包模态识别方法（CMICW），并进行了实验验证。总之，该方法成功地识别了桥梁的固有频率和绝对振型，具有较高的精度。值得注意的是，这些结果是第一次从移动传感器数据中提取扭转模态形状信息。此外，车辆速度对估计精度的影响进行了研究。最后，提出了一种混合仿真框架，以考虑原始移动传感数据内的车辆动力学。该方法在消除车辆动力学效应和识别模态特性方面是成功的。这些结果有助于不断增长的知识体系的实践中的移动crowdsensing交通基础设施的物理特性。
英文摘要：
This study presents a flexible approach for bridge modal identification using smartphone data collected by a large pool of passing vehicles. With each trip of a mobile sensor, the spatio-temporal response of the bridge is sampled, plus various sources of noise, e.g., vehicle dynamics, environmental effects, and road profile. This paper provides further evidence to support the hypothesis that through trip aggregation, such noise effects can be mitigated and the true bridge dynamics are exhibited. In this study, the continuous wavelet transform is applied to each trip, and the results are combined to estimate the structural modal response of the bridge. The Crowdsourced Modal Identification using Continuous Wavelets (CMICW) method is presented and validated in an experimental setting. In summary, the method successfully identifies natural frequencies and absolute mode shapes of a bridge with high accuracy. Notably, these results are the first to extract torsional mode shape information from mobile sensor data. Moreover, the influence of vehicle speed on the estimation accuracy is investigated. Finally, a hybrid simulation framework is proposed to account for the vehicle dynamics within the raw mobile sensing data. The proposed method is successful in removing vehicle dynamic effects and identifying modal properties. These results contribute to the growing body of knowledge on the practice of mobile crowdsensing for physical properties of transportation infrastructure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
