Power spectrum density transmissibility (PSDT) functions have attracted widespread attention in operational modal analysis (OMA) because of their robustness to excitations. However, the selection of the peaks and stability axes are still subjective and requires further investigation. To this end, this study took advantage of PSDT functions and support-vector machines (SVMs) to propose a two-stage automated modal identification method. In the first stage, the automated identification of peaks is achieved by introducing the peak slope (PS) as a critical index and determining its threshold using the SVM classifier. In the second stage, the automated identification of the stability axis is achieved by introducing the relative difference coefficients (RDCs) of the modal parameters as indicators and determining their thresholds using the SVM classifier. To verify its feasibility and accuracy, the proposed method was applied to an ASCE-benchmark structure in the laboratory and in a high-rise building installed with a structural health monitoring system (SHMS). The results showed that the automated identification method could effectively eliminate spurious modes and accurately identify the closely spaced modes. The proposed method can be automatically applied without manual intervention, and it is robust to noise. It is promising for application to the real-time condition evaluation of civil structures installed with SHMSs.

中文翻译：

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基于功率谱密度传递率和支持向量机的两阶段自动运行模态分析

功率谱密度传递率 (PSDT) 函数因其对激励的鲁棒性而在操作模态分析 (OMA) 中引起了广泛关注。然而，峰值和稳定性轴的选择仍然是主观的，需要进一步研究。为此，本研究利用PSDT函数和支持向量机（SVM）提出了一种两阶段自动模态识别方法。在第一阶段，通过引入峰值斜率 (PS) 作为关键指标并使用 SVM 分类器确定其阈值来实现峰值的自动识别。在第二阶段，通过引入模态参数的相对差异系数（RDC）作为指标并使用SVM分类器确定其阈值来实现稳定性轴的自动识别。为了验证其可行性和准确性，将所提出的方法应用于实验室中的 ASCE 基准结构和安装有结构健康监测系统 (SHMS) 的高层建筑中。结果表明，该自动识别方法能够有效消除杂散模态，准确识别间距较近的模态。所提出的方法无需人工干预即可自动应用，并且对噪声具有鲁棒性。在安装 SHMS 的土木结构实时状态评估中的应用前景广阔。结果表明，该自动识别方法能够有效消除杂散模态，准确识别间距较近的模态。所提出的方法无需人工干预即可自动应用，并且对噪声具有鲁棒性。在安装 SHMS 的土木结构实时状态评估中的应用前景广阔。结果表明，该自动识别方法能够有效消除杂散模态，准确识别间距较近的模态。所提出的方法无需人工干预即可自动应用，并且对噪声具有鲁棒性。在安装 SHMS 的土木结构实时状态评估中的应用前景广阔。