Guangzhou University-Tamkang University Joint Research Center for Engineering Structure Disaster Prevention and Control, Guangzhou University, Guangzhou, Guangdong 510006, China.
Math Biosci Eng. 2019 Jun 19;16(5):5709-5728. doi: 10.3934/mbe.2019285.
It is usually of great importance to identify modal parameters for dynamic analysis and vibration control of civil structures. Unlike the cases in many other fields such as mechanical engineering where the input excitation of a dynamic system may be well quantified, those in civil engineering are commonly characterized by unknown external forces. During the last two decades, stochastic subspace identification (SSI) method has been developed as an advanced modal identification technique which is driven by output-only records. This method combines the theory of system identification, linear algebra (e.g., singular value decomposition) and statistics. Through matrix calculation, the so-called system matrix can be identified, from which the modal parameters can be determined. The SSI method can identify not only the natural frequencies but also the modal shapes and damping ratios associated with multiple modes of the system simultaneously, making it of particular efficiency. In this study, main steps involved in the modal identification process via the covariance-driven SSI method are introduced first. A case study is then presented to demonstrate the accuracy and efficiency of this method, through comparing the corresponding results with those via an alternative method. The effects of noise contaminated in output signals on identification results are stressed. Special attention is also paid to how to determine the mode order accurately.
识别模态参数对于土木结构的动力分析和振动控制通常非常重要。与机械工程等许多其他领域的情况不同，在这些领域中，动力系统的输入激励可能可以很好地量化，但土木结构中的情况通常具有未知的外力。在过去的二十年中，随机子空间识别（SSI）方法已发展成为一种先进的模态识别技术，它由仅输出记录驱动。该方法结合了系统识别理论、线性代数（例如奇异值分解）和统计学。通过矩阵计算，可以识别出所谓的系统矩阵，从中可以确定模态参数。SSI 方法不仅可以识别自然频率，还可以同时识别与系统多个模态相关的模态形状和阻尼比，因此效率特别高。在本研究中，首先介绍了通过协方差驱动的 SSI 方法进行模态识别的主要步骤。然后通过与替代方法的结果进行比较，通过案例研究展示了该方法的准确性和效率。强调了输出信号中噪声污染对识别结果的影响。还特别注意如何准确确定模态阶数。
