Estimating dynamic characteristics of four-story reinforced concrete building in full-scale shaking table tests using system identification methods

초록

This study investigates the dynamic characteristics of a full-scale four-story reinforced concrete building using system identification, focusing on short-term measurements for future integration into a cyber-physical system. The building, consisting of a moment-resisting and shear wall frame, was subjected to large-scale shaking table tests to simulate damage scenarios ranging from slight cracking to near collapse using historical earthquake ground motions. Changes in natural periods, damping ratios, and mode shapes were analyzed before and after seismic events using nine system identification methods, including four input-output methods (transfer function (TF), eigen realization algorithm with observer/Kalman filter identification (ERA-OKID) for input-output system, autoregressive with exogenous input (ARX), and numerical algorithms for state-space subspace system identification (N4SID) for input-output system) and five output-only system methods (ERA-OKID for output-only system, N4SID for output-only system, autoregressive (AR), enhanced frequency-domain decomposition (EFDD) and covariance-driven stochastic subspace identification (SSI-COV)). The results demonstrate that even with measurement times of less than 1 min, the proposed method accurately identifies the first-mode characteristics. Computational efficiency was also assessed, with TF and EFDD emerging as the fastest algorithms and SSI-COV providing the most reliable results. These findings demonstrate the potential of short-term system identification for efficient structural health monitoring, providing a basis for its future integration into cyber-physical systems.

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