基于车辆加速度测量的桥梁模态识别
Bridge Modal Identification using Acceleration Measurements within Moving Vehicles
摘要：
通过桥梁结构的车辆对桥梁的振动作出动态响应。在移动车辆内收集的加速度信号包含桥梁结构响应的轨迹，但也包括其他来源，如车辆悬架系统和表面粗糙度引起的振动。介绍了利用移动车辆网络收集的数据进行桥梁系统辨识的两种方法。通过在频域中对车辆响应进行去卷积来去除车辆悬架系统的贡献。第一种方法利用车辆传递函数，第二种方法使用EEMD方法。其次，粗糙度引起的振动提取使用二阶盲识别（SOBI）方法。在这两个过程之后，产生的信号相当于扫描桥梁动态响应的移动传感器的读数。利用移动传感器数据的结构模态识别最近已被引入作为CFDEX算法。处理后的移动传感器数据进行了分析，使用ADDTEX来识别桥梁的模态特性。通过移动车辆网络监测的长单跨桥梁的数值案例研究的方法的性能进行了验证。分析考虑三种路面粗糙度模式。结果表明，所提出的算法是成功的提取纯桥梁振动，并产生准确和全面的桥梁模态特性。研究表明，所提出的传递函数方法可以有效地反卷积运动车辆的线性动力学。EEMD方法能够在没有车辆先验信息的情况下提取车辆的动态响应。这项研究是第一个提出的完整的桥梁模态识别方法，包括使用\textit{moving vehicles sensor data}的工作自然频率，模态振型和阻尼比。
英文摘要：
Vehicles crossing bridge structures respond dynamically to the bridge's vibrations. An acceleration signal collected within a moving vehicle contains a trace of the bridge's structural response, but also includes other sources such as the vehicle suspension system and surface roughness-induced vibrations. This paper introduces two methods for the bridge system identification using data collected by a network of moving vehicles. The contributions of the vehicle suspension system are removed by deconvolving the vehicle response in frequency domain. The first approach utilizes the vehicle transfer function, and the second uses EEMD method. Next, roughness-induced vibrations are extracted using second-order blind identification (SOBI) method. After these two processes the resulting signal is equivalent to the readings of mobile sensors that scan the bridge's dynamic response. Structural modal identification using mobile sensor data has been recently introduced as STRIDEX algorithm. The processed mobile sensor data is analyzed using STRIDEX to identify the modal properties of the bridge. The performance of the methods is validated on numerical case studies of a long single-span bridge monitored via a network of moving vehicles. The analyses consider three road surface roughness patterns. Results show that the proposed algorithms are successful in extracting pure bridge vibrations, and produce accurate and comprehensive modal properties of the bridge. The study shows that the proposed transfer function method can efficiently deconvolve the linear dynamics of a moving vehicle. EEMD method is able to extract vehicle dynamic response without a-priori information about the vehicle. This study is the first proposed methodology for complete bridge modal identification, including operational natural frequencies, mode shapes and damping ratios using \textit{moving vehicles sensor data}.
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。
