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<p>Modal analysis, which provides modal parameters including frequencies, damping ratios, and mode shapes, is essential for assessing structural safety in structural health monitoring. Automated operational modal analysis (AOMA) offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment. However, estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions. To address this issue, we propose an automated modal identification approach comprising three key aspects: (1) identification of modal parameters using covariance-driven stochastic subspace identification; (2) automated interpretation of the stabilization diagram; (3) an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure (OPTICS) combined with <i>k</i>-nearest neighbors (KNN). The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention. A simulated 10-story shear frame was used to verify the methodology. Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice. The proposed approach can automatically identify modal parameters with high accuracy, making it suitable for a real-time structural health monitoring framework.</p>

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A two-stage framework for automated operational modal identification using OPTICS-KNN-based clustering

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Yi Chen,
  • Wenwei Fu,
  • Yaozhi Luo,
  • Yanbin Shen,
  • Hui Yang,
  • Shiying Wang

摘要

Modal analysis, which provides modal parameters including frequencies, damping ratios, and mode shapes, is essential for assessing structural safety in structural health monitoring. Automated operational modal analysis (AOMA) offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment. However, estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions. To address this issue, we propose an automated modal identification approach comprising three key aspects: (1) identification of modal parameters using covariance-driven stochastic subspace identification; (2) automated interpretation of the stabilization diagram; (3) an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure (OPTICS) combined with k-nearest neighbors (KNN). The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention. A simulated 10-story shear frame was used to verify the methodology. Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice. The proposed approach can automatically identify modal parameters with high accuracy, making it suitable for a real-time structural health monitoring framework.