Three-stage automatic operational modal analysis using mathematical mode elimination by density-based clustering method - A. Salar Mehrabad; A. Shooshtari (مهندسی عمران شریف)
Estimating a structure's modal parameters is essential for various applications, including health monitoring, damage detection, design verification, and model updating. Modal parameters are a structure's natural frequencies, mode shapes, and damping ratios. They can be used to understand the structure's dynamic behavior and to identify any changes that may occur over time. Operational modal analysis (OMA) is a technique that uses the response of a structure to environmental loads to estimate modal parameters. OMA is a non-destructive testing method that can be used on structures in their operating environment. This makes it a valuable tool for health monitoring and damage detection of buildings, bridges, wind turbines, and stadiums. One of the challenges of OMA is that its methods rely on the user's judgment to separate physical modes from spurious modes and to distinguish between real modes of the structure. Spurious modes are not caused by the actual structure but by noise or other environmental factors. Real modes are caused by the structure itself. In recent years, there has been extensive research on automating OMA methods for modal parameter estimation. Most of these studies have attempted to minimize the need for user intervention in modal parameter calculation by using machine learning techniques. Machine learning techniques can be used to identify physical modes automatically and to distinguish between real modes of the structure. This research uses the Stochastic Subspace Identification (SSI) method for OMA. The DBSCAN clustering method is used to separate physical modes from spurious modes. Finally, the hierarchical clustering method is used to distinguish between real modes of the structure. The proposed algorithm was implemented on a 6-degree-of-freedom structure and a real bridge. The results show that the proposed method has a higher power to separate physical modes from spurious modes than previous methods.