Automated Operational Modal Analysis of Bridge Structure Based on Infinite Dirichlet Process Mixture Model Without Prior Threshold

As one of the vital steps for structural health monitoring, automated operational modal analysis (AOMA) without manual intervention remains one of the challenging problems because it requires processing large datasets and involves many user‐defined thresholds. Combining the covariance‐driven stochastic subspace identification (Cov‐SSI) with the Dirichlet process mixture model (DPMM), a novel AOMA is proposed to cluster the stabilization diagram (SD) for automatically identifying structural modal parameters without prior thresholds. Based on the current Chinese specifications, the physical property of the mode shapes, and the uncertainty of the frequencies, the hard and soft validation criteria are determined to cleanse the initial SD derived from the Cov‐SSI algorithm. The clustering datasets with two‐dimensional clustering features are subsequently constructed by the frequency and damping ratio information included in the stable poles of the cleansed SD. Then, the DPMM, which optimizes simultaneously the cluster count and results, is incorporated into the automatic clustering process of the cleaned SD. The DPMM is iteratively solved using the collapsed Gibbs sampling method, and based on this, a novel AOMA approach for bridge structures is proposed, which requires only one‐time clustering operation. Moreover, the impact of the time lag in Cov‐SSI and hyperparameter α in the Dirichlet process on the automated clustering is also discussed. The proposed AOMA based on DPMM is initially validated using the measured data from the Dowling Hall Footbridge, which features two medium‐span steel truss girders. Subsequently, this method is applied to a practical long‐span flexible suspension bridge with a main span of 660 m. Validation and practical application results indicate that the proposed algorithm can accurately, efficiently, and automatically identify the modal parameters of the bridge structures with dense modes.