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

Yi He, Judy P. Yang*, Yi-Feng Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number113891
JournalEngineering Structures
Volume255
DOIs
StatePublished - 15 Mar 2022

Keywords

  • Automated modal identification
  • Bridge modal analysis
  • DBSCAN algorithm
  • Frequency uncertainty
  • Spurious mode

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