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

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.

Automated summary

This study proposes a framework for automated modal identification of bridge parameters, based on the uncertainty of estimated frequencies and density-based clustering algorithm. The framework calculates the modal parameters and standard deviations of the estimated frequencies to construct a stabilization diagram, eliminates spurious modes using criteria of frequency uncertainty and stabilization, and employs a modified version of an unsupervised density-based clustering algorithm for automated identification.