A three-stage automated modal identification framework for bridge parameters based on frequency uncertainty and density clustering

• Published in: Engineering structures Vol. 255; p. 113891
• Main Authors: He, Yi, Yang, Judy P., Li, Yi-Feng
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.03.2022; Elsevier BV
• Subjects: Algorithms; Automated modal identification; Automation; Bridge modal analysis; Clustering; Data analysis; DBSCAN algorithm; Density; Frequency uncertainty; Modal analysis; Modal identification; Outliers (statistics); Parameter identification; Spurious mode; Stabilization; Uncertainty; Bridge modal analysis; DBSCAN algorithm; Frequency uncertainty; Spurious mode; Automated modal identification
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2022.113891

•A three-stage automated modal identification framework is proposed on the basis of frequency uncertainty and density-based clustering.•The frequency uncertainty criterion removes most spurious modes and guarantees reliability of identified modal parameters.•The modified version of DBSCAN algorithm remains robust even subject to the interference of spurious modes.•The propose framework can be applied to other civil structures in addition to the bridges. As the automated modal analysis is crucial for a continuous monitoring system, this study proposes a framework for automated modal identification of bridge parameters based on the uncertainty of estimated frequencies and density-based clustering algorithm, which consists of the following three stages: First, the modal parameters and standard deviations of the estimated frequencies are calculated in a wide range of model orders to construct the stabilization diagram using the reference-based covariance-driven stochastic subspace identification algorithm. Second, the criteria of frequency uncertainty and stabilization are adopted to eliminate the spurious modes. Third, for present purpose, the modified version of an unsupervised density-based clustering algorithm is introduced to group physical modes and detect outliers to reach automated identification of bridge modal parameters. From the analysis, it has shown that the proposed framework is powerful in eliminating the spurious modes and robust in the presence of interference caused by spurious modes while a simple procedure for clustering physical modes with desired statistical reliability is employed.