Synergistic bridge modal analysis using frequency domain decomposition, observer Kalman filter identification, stochastic subspace identification, system realization using information matrix, and autoregressive exogenous model. (November 2021) - Tran, Thanh T.X.; Ozer, Ekin
Highlights: Five different identification methods (FDD, OKID, SSI, SRIM, ARX) are performed to extract structural modal features. A quantitative, ranking-based collocation framework is pursued to merge different algorithm findings and reduce method-specific modal identification uncertainties with quantified confidence on the outranking method. The framework is verified with a numerical example and applied on two realistic testbeds, one large-scale shaking table test of a reinforced concrete bridge and another steel pedestrian bridge. The applications demonstrate improved identification results, including damage and no damage cases in terms of true positives and true negatives. Abstract: This paper presents multiple system identification of large-scale bridge structures proposing the combined usage of different modal parameter findings, namely from Frequency Domain Decomposition, Observer Kalman Filter Identification/Eigensystem Realization Algorithm, Combined Deterministic Stochastic Subspace Identification, System Realization Using Information Matrix, and Autoregressive Exogenous Model. A method-centric democratic ranking approach visualizes and quantifies the harmony among different system identification methods in terms of modal parameters, then ranks them based on the correlation among each other, and consequently complies with the highest rank modal parameter outputs. The synergistic scheme is applied on a numerical beam and two bridge structures including one healthy and another subjected to progressive damage. Looking at the top-rank selections, one can see that outlier identification results from a population of modal parameters can intuitively become extinct. The collaboration among methods is dependent on the chosen methods; therefore, method selection relies on care and fair representation of the identification features. Lack of agreement between methods can indicate low confidence in the outranking method and is quantified by median absolute deviation. Nevertheless, the majority of the algorithm population agrees on specific results, which are valuable to produce state knowledge despite low signal to noise ratio, especially without the presence of a reference. Thus, the collaborative usage of multiple methods in a systematic and ranking-based manner reduces significant error and outlier possibilities in modal identification due to algorithm-related issues, which is the novel contribution of this study.