Identification of Parametric Variations of Structures Based on Least Squares Estimation and Adaptive Tracking Technique

An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to detect the damage when it occurs. System identification and damage detection, based on measured vibration data, have received considerable attention recently. Frequently, the damage of a structure may be reflected by a change of some parameters in structural elements, such as a degradation of the stiffness. Hence it is important to develop data analysis techniques that are capable of detecting the parametric changes of structural elements during a severe event, such as the earthquake. In this paper, we propose a new adaptive tracking technique, based on the least-squares estimation approach, to identify the time-varying structural parameters. In particular, the new technique proposed is capable of tracking the abrupt changes of system parameters from which the event and the severity of the structural damage may be detected. The proposed technique is applied to linear structures, including the Phase I ASCE structural health monitoring benchmark building, and a nonlinear elastic structure to demonstrate its performance and advantages. Simulation results demonstrate that the proposed technique is capable of tracking the parametric change of structures due to damages.

[1]  Yu Lei,et al.  Hilbert-Huang Based Approach for Structural Damage Detection , 2004 .

[2]  Erik A. Johnson,et al.  Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data , 2004 .

[3]  Dionisio Bernal,et al.  Flexibility-Based Approach for Damage Characterization: Benchmark Application , 2004 .

[4]  Gregory W. Reich,et al.  Structural system identification: from reality to models , 2003 .

[5]  Xuemin Shen,et al.  Adaptive fading Kalman filter with an application , 1994, Autom..

[6]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[7]  P. Frank,et al.  Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis , 1996 .

[8]  Shuwen Pan,et al.  Identification and tracking of structural parameters with unknown excitations , 2004, Proceedings of the 2004 American Control Conference.

[9]  C. Loh,et al.  Time Domain Identification of Frames under Earthquake Loadings , 2000 .

[10]  Karl Johan Åström,et al.  Self-Tuning Regulators—Design Principles and Applications , 1979 .

[11]  Elias B. Kosmatopoulos,et al.  Development of adaptive modeling techniques for non-linear hysteretic systems , 2002 .

[12]  Roger Ghanem,et al.  Structural System Identification. II: Experimental Verification , 1995 .

[13]  Chin-Hsiung Loh,et al.  A system identification approach to the detection of changes in both linear and non‐linear structural parameters , 1995 .

[14]  Zhikun Hou,et al.  Application of Wavelet Approach for ASCE Structural Health Monitoring Benchmark Studies , 2004 .

[15]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[16]  M. Hoshiya,et al.  Structural Identification by Extended Kalman Filter , 1984 .

[17]  Andrew W. Smyth,et al.  On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems , 1999 .

[18]  N. Huang,et al.  System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes , 2003 .

[19]  Richard W. Longman,et al.  On‐line identification of non‐linear hysteretic structural systems using a variable trace approach , 2001 .

[20]  Erik A. Johnson,et al.  NATURAL EXCITATION TECHNIQUE AND EIGENSYSTEM REALIZATION ALGORITHM FOR PHASE I OF THE IASC-ASCE BENCHMARK PROBLEM: SIMULATED DATA , 2004 .

[21]  James L. Beck,et al.  Preface to the Special Issue on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark , 2004 .

[22]  Bijan Samali,et al.  Benchmark Problem for Response Control of Wind-Excited Tall Buildings , 2004 .

[23]  Kai Qi,et al.  Adaptive H ∞ Filter: Its Application to Structural Identification , 1998 .