Robust Automated Operational Modal Analysis Framework Based on Enhanced Stabilization Diagram and Hierarchical Density Estimation

Developing fully automated operational modal analysis (AOMA) algorithms is a critical yet challenging task in structural health monitoring, with urgent practical engineering demands. This study introduces a robust AOMA framework that leverages an enhanced stabilization diagram and hierarchical density estimation strategy to address these challenges. The main innovations of this framework are threefold: (1) A comprehensive physical mode validation strategy that effectively eliminates more stubborn spurious poles by destroying the noise structure inherent in the identified system matrix. (2) A hierarchical density clustering approach for the automatic interpretation of stabilization diagrams, which eliminates the need for manual threshold selection and adapts seamlessly to varying-density clustering scenarios. (3) A novel representative mode selection approach based on clustering exemplars is presented, resulting in a stronger consistency of the selected modal parameters. Hierarchical clustering of modal poles, optimal cutting of clustering trees, outlier rejection, and cluster quality validation are integrated in a single framework, streamlining the analysis and avoiding tedious postprocessing steps. The robustness and applicability of the algorithm are extensively validated using a numerical building structure, the Z24 bridge benchmark test, and a footbridge equipped with a long-term continuous monitoring system. The results demonstrate that the proposed framework achieves robust AOMA on long-term field measurement data without any user intervention. The applicability of the algorithm to closely spaced modes and long-term modal tracking tasks is also demonstrated. This study advances the field of AOMA by offering a scalable, efficient, and accurate solution for real-time structural health assessment, with potential extensions to broader engineering applications.