Abstract
Abstract A new fully automated operational modal analysis (AOMA) algorithm based on the time-domain covariance-driven stochastic subspace identification (cov-SSI) system identification method is proposed. The new algorithm is intended for large civil structures, such as long-span suspension bridge for which an application example is included. It is shown that the new algorithm is capable of consistently detecting all expected structural modes of the Hardanger bridge, without requiring any prior tuning or parameter selection. The dominating three-step approach to AOMA algorithms in published literature on this topic is highlighted and used as a basis for the new algorithm; it first removes spurious poles using a Gaussian mixture model by analysing their distance to their nearest neighbour. Secondly, hierarchical clustering is used to regroup similar poles, and finally, the groups representing physical modes are selected through a similarity analysis of the size of hierarchical clusters.
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