Application of Recursive Stochastic Subspace Identification in Structural Damage Localization
|關鍵字:||唯輸出;隨機子空間識別;遞迴式子空間系統識別;系統識別;結構損傷探測;output-only;stochastic subspace identification;recursive stochastic subspace identification;system identification;structural damage detection|
This study integrates the Recursive Stochastic Subspace Identification (RSSI) with the method of state-space damage localization vector (DLV) for structural damage detection which in turn serves as the basis for the development of a real-time (on-line) structural health monitoring system. The Recursive Stochastic Subspace Identification is developed based on the theory of Projection Approximation Subspace Tracking (PAST), along with the concepts of Instrumental Variable and Covariance-driven Stochastic Subspace Identification (SSI-COV). Via numerical simulations as well as the shaking table tests conducted in NCREE using the global responses of the structure under seismic excitation, the proposed scheme has been proved sufficient, without knowledge of the input, for on-line identification of the system parameters, and by which the damaged stories of the structure could be reliably located in most conditions. This study also explores issues on the tracking capability of the RSSI-COV for variable systems, the effects of noises and the choice of initial matrix. The RSSI-COV is found capable of tracking the frequency change of a variable system, confirming its potential in on-line system identification, yet it seems not quite sufficient in identifying the mode shapes with desired accuracy. It is believed that the result could be improved if the input signal is considered in the analysis. Moreover, it has been shown that the results of damage detection will not be affected if the noise-to-signal ratio (NSR) is under 20 percent, revealing the robustness of the RSSI-COV in noise filtering. In addition, adopting a small value random matrix as the initial values leads to good identification results, regardless of time-varying or time-invariant systems. .