Damage identification of a benchmark building for structural health monitoring

An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to evaluate its possible damage. Recently, an IASC–ASCE benchmark problem for structural health monitoring has been developed in order to facilitate the comparison of various analysis techniques for the damage identification of structures on a common basis. The technique of Hilbert–Huang transform (HHT) has been shown to be a possible system identification method for linear structures. This paper presents the application of HHT to the phase I IASC–ASCE benchmark building for the complete identification of stiffness and damping coefficients. The cases analyzed involve the damage assessment in the weak direction of the benchmark building using 12-DOF and 120-DOF models. In this benchmark problem, the structural parameters, including the stiffness and damping, before and after damage are identified first, and then the location and severity of the damage are assessed by a comparison. The effect of measurement noise has been taken into account. Simulation results demonstrate that the accuracy of the HHT technique presented in identifying the structural parameters is quite plausible, and it represents a possible damage detection technique for linear structures.

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

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

[3]  Lambros S. Katafygiotis,et al.  Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study , 2004 .

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

[5]  Jun Yu,et al.  Investigation of a System Identification Methodology in the Context of the ASCE Benchmark Problem , 2004 .

[6]  C. Farrar,et al.  SYSTEM IDENTIFICATION FROM AMBIENT VIBRATION MEASUREMENTS ON A BRIDGE , 1997 .

[7]  Thomas G. Carne,et al.  Modal parameter extraction from large operating structures using ambient excitation , 1995 .

[8]  Thomas G. Carne,et al.  The Natural Excitation Technique (NExT) for modal parameter extraction from operating wind turbines , 1993 .

[9]  James L. Beck,et al.  Structural Health Monitoring Using Ambient Vibrations , 1998 .

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

[11]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

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

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

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

[15]  James L. Beck,et al.  Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies , 2004 .

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

[17]  Mohammad Noori,et al.  Wavelet-Based Approach for Structural Damage Detection , 2000 .

[18]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.