A two-stage framework for automated operational modal identification

Automated operational modal analysis (OMA) is attractive and has been extensively used to replace traditional OMA, which requires much empirical observation and engineers' judgment. Still, the uncertainties on modal parameters and spurious modes are challenging to estimate under the field conditions. For addressing this challenge, this research proposed an automated modal identification approach. The proposed approach consists of two steps: (1) modal analysis using covariance-driven stochastic subspace algorithm; (2) automated interpretation of the stabilization diagram. An additional uncertainty criterion is employed to initially remove as many spurious modes as possible. A novel threshold calculation for clustering is proposed with incorporating uncertainty of modal parameters and the weighting factor. An improved self-adaptive clustering with new distance calculation is used to group physical modes, followed by the final step of robust outlier detection to select outlying modes. The proposed automated approach requires minimum human intervention. Two field tests of the footbridge and a post-tensioned concrete bridge are used to verify the proposed approach. A modal tracking was used for continuously measured data for demonstrating the applicability of the approach. Results show the proposed approach has fairly good performance and be suitable for automated OMA and long-term health monitoring.