A Two-stage Framework for Automated Operational Modal Identification

Abstract

Automated operational modal analysis (OMA) is attractive and has been extensively used to replace traditional OMA, which involves much empirical observation and engineers’ judgment. However, the uncertainties on modal parameters and spurious modes are still 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 (SSI-cov/ref); (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.

Probabilistic damage detection and identification of coupled structural parameters using Bayesian model updating with added mass

“…𝜅 consists of 𝜎 ,,' and 𝜎 '.,' ; 𝜎 𝑓,𝑚 and 𝜎 '.,' are the standard derivation of the 𝑚th measured frequency and mode shape, respectively. These two weighting factors can be identified by either Bayesian modal analysis [41] or stochastic subspace identification (SSI) based uncertainty analysis [42], rather than manually tunning.…”

Probabilistic Damage Detection and Identification of Coupled Structural Parameters using Bayesian Model Updating with Added Mass

“…consists of ,,' and '.,' ; , and '.,' are the standard derivation of the th measured frequency and mode shape, respectively. These two weighting factors can be identified by either Bayesian modal analysis [41] or stochastic subspace identification (SSI) based uncertainty analysis [42], rather than manually tunning. For avoiding intractable high-dimensional integrals, MCMC is employed to approximate the posterior PDF in Eq.…”

“…The automated stochastic subspace identification (SSI) [48] is used to identify modal parameters, e.g., natural frequencies and mode shapes, and associated uncertainties. Uncertainties on modal parameters measure modal parameters' accuracy and can be used as weighting factors, such as in Eq.…”

Machine learning‐based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges

“…He et al 19 applied a modified version of fuzzy C‐means (FCM) clustering to the Fourth Nanjing Yangtze River Bridge. Zeng and Hoon Kim 20 tested a self‐adaptive clustering approach with a weighted multi‐term distance on the Z24 and Downing Hall benchmarks. The classic hierarchical clustering‐based AOMA was applied by Anastasopoulos et al 21 to almost one year of acquisitions from a steel single‐span tied arch railway bridge.…”