Demirlioglu Kultigin, Gonen Semih, Erduran Emrah
Department of Built Environment, Oslo Metropolitan University, P.O. Box 4, NO-0130 Oslo, Norway.
Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya (UPC-BarcelonaTech), 08034 Barcelona, Spain.
Sensors (Basel). 2023 Jul 12;23(14):6335. doi: 10.3390/s23146335.
This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques.
本研究系统地评估了车辆扫描方法(VSM)在准确估计弹性支座上桥梁振型方面的有效性。采用三种先进的VSM方法,利用车辆行驶过程中的数据来获取桥梁的振型。其中两种评估方法使用信号分解技术从响应的接触点提取桥梁的模态分量,而第三种方法则依赖于沿桥面分割测量信号,并对每个分割信号应用运行模态分析工具来估计振型。对一座单跨桥和一座双跨桥进行了数值分析,考虑了平滑和粗糙的路面轮廓、不同的车速以及前车的存在。测试了用于开发和评估车辆扫描模型的数值模型的准确性,研究结果表明,使用半车模型和信号分解技术的方法在车速和路面粗糙度增加时表现出鲁棒性,而应用测量信号分割的方法在低速时能在桥边提供相对准确的振型估计,尽管这三种方法都有其局限性。还观察到,简化的桥梁和车辆模型可能会掩盖实际车辆和桥梁系统复杂性所带来潜在挑战。鉴于全球大量桥梁是建在弹性支座上的,车辆扫描方法的实际成功取决于其可靠处理弹性边界条件的能力。因此,本文为当前车辆扫描方法的能力和局限性提供了有价值的见解,为这些技术的进一步发展和完善铺平了道路。