Application of the on‐line recursive least‐squares method to perform structural damage assessment

The identification of structural damage is an important objective of health monitoring for civil infrastructure. An on-line recursive least-squares (RLS) identification technique is applied in this study to identify the time-varying dynamic parameters of structures subjected to earthquake loadings. Based on the framework of adaptive filters, the observations are obtained sequentially in real time. It is desirable to perform the identification tasks recursively to save computation time and to be able to observe the variations of parameters on-line. Computation of the classical least-squares (LS) method can be arranged recursively so that the estimated parameters at previous step can be used to predict the responses at current time. The one-step ahead predicted error between estimated response and measured response is calculated by the on-line RLS method and the dynamic properties of system can be identified as well. The purpose of this study is to apply the RLS method to verify the identification procedures and to perform the damage assessment on a three-floor shaking table benchmark model tested at NCREE in Taiwan. Furthermore, both classical LS and RLS methods are implemented to investigate the recorded strong-motion data of Tai-Tung Fire Bureau Building located at Tai-Tung City in Taiwan, which had been demolished due to severe damage after a magnitude 6.2 earthquake in 2006. By observing the variations of the identified time-varying modal properties of both benchmark model and real building, global damage behavior due to weak element or failure of components can be revealed. Copyright © 2009 John Wiley & Sons, Ltd.

[1]  John B. Mander,et al.  A simple LMS‐based approach to the structural health monitoring benchmark problem , 2005 .

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

[3]  Zushu Li,et al.  Investigation of attitude harmony control of full car for Magnetorheological semi-active suspension , 2008 .

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

[5]  J. Geoffrey Chase,et al.  Efficient structural health monitoring for a benchmark structure using adaptive RLS filters , 2005 .

[6]  Satish Nagarajaiah,et al.  H_{-}/H_{infty } structural damage detection filter design using an iterative linear matrix inequality approach , 2008 .

[7]  Peter Andersson,et al.  Adaptive Forgetting in Recursive Identification through Multiple Models , 1985 .

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

[9]  Satish Nagarajaiah,et al.  Real-Time Structural Damage Monitoring by Input Error Function , 2005 .

[10]  Satish Nagarajaiah,et al.  Structural Health Monitoring using ARMarkov Observers , 2006 .

[11]  Shih-Yu Chu,et al.  Application of on-line RLS on parameter identification of benchmark building structure on shaking table test , 2008 .

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

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

[14]  Satish Nagarajaiah,et al.  Structural damage detection using decentralized controller design method , 2008 .

[15]  Raimondo Betti,et al.  On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach , 2004 .

[16]  Li Zhou,et al.  An adaptive extended Kalman filter for structural damage identification , 2006 .

[17]  Shih Chieh Lo,et al.  Application of real‐time adaptive identification technique on damage detection and structural health monitoring , 2009 .

[18]  Jann N. Yang,et al.  Identification of Parametric Variations of Structures Based on Least Squares Estimation and Adaptive Tracking Technique , 2005 .

[19]  Chung Bang Yun,et al.  Identification of Linear Structural Dynamic Systems , 1982 .

[20]  Robin Huckaby Preston.  LMS-based method for damage detection applied to Phase II of Structural Health Monitoring benchmark problem , 2006 .

[21]  Chin-Hsiung Loh,et al.  Application of off-line and on-line identification techniques to building seismic response data , 1996 .

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

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

[24]  Masanobu Shinozuka,et al.  Experimental Verification of a Number of Structural System Identification Algorithms , 1991 .

[25]  Hannes A. Spieth,et al.  LMS-based structural health monitoring of a non-linear rocking structure , 2005 .