Modal parameter estimation of a bridge with the vibration responses measured from an instrumented vehicle moving at a controlled speed is an active area of research. It is challenging to determine bridge natural frequencies and their associated mode shapes, as the measured output-only responses of an instrumented vehicle contain the desired dynamic responses of the bridge, along with the other confounding components related to vehicle dynamics, driving frequency component, and dynamics associated with the roughness profile of the road. These bridge responses are often masked by the dynamic responses associated with the vehicle, and road surface profile. Measurement noise components add to the existing problem of separating bridge frequency from various other said components. In this paper, an attempt has been made to extract bridge frequencies and mode shapes through the output-only responses collected from a traversing vehicle and using singular value decomposition (SVD) combined with the Teager-Kaiser energy operator (TKEO). Numerical investigations are made on the proposed SVD-TKEO-based modal identification technique in the presence of measurement noise. Parametric studies are conducted to investigate the influence of vehicle speed and road surface roughness on the quality of the identified bridge modal parameters using the proposed technique Numerical simulations carried out, show that the proposed SVD-TKEO-based algorithm performs well in identifying bridge mode shapes, even with relatively higher vehicle traveling speed, and handles even roughness of the road surface profile reasonably well. Lab-level experimental studies using vehicle bridge interaction setup, are also carried out using the SVD-based modal parameter estimation technique to explore its practical use.

利用以受控速度移动的仪表车辆测量的振动响应来估计桥梁的模态参数是一个活跃的研究领域。确定桥梁固有频率及其相关模态形状具有挑战性，因为测量的仪表车辆的仅输出响应包含桥梁所需的动态响应，以及与车辆动力学、驾驶频率分量和相关的其他混杂分量。与道路粗糙度轮廓相关的动力学。这些桥梁响应通常被与车辆和路面轮廓相关的动态响应所掩盖。测量噪声分量增加了将电桥频率与各种其他所述分量分开的现有问题。在本文中，尝试通过从行驶车辆收集的仅输出响应并使用奇异值分解（SVD）结合 Teager-Kaiser 能量算子（TKEO）来提取桥频率和振型。在存在测量噪声的情况下，对所提出的基于 SVD-TKEO 的模态识别技术进行了数值研究。进行了参数研究，以调查车速和路面粗糙度对使用所提出的技术识别桥梁模态参数质量的影响进行的数值模拟表明，所提出的基于 SVD-TKEO 的算法在识别桥梁模态形状方面表现良好，即使在相对较高的车辆行驶速度下，也能很好地处理路面轮廓的均匀粗糙度。使用车桥交互装置进行实验室级实验研究，并使用基于 SVD 的模态参数估计技术来探索其实际用途。