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.