Structural Nonlinear Damage Detection Method Using AR/ARCH Model

Structural health monitoring (SHM) has received increasing attention in the research community over the past two decades. Most of the relevant research focuses on linear structural damage detection. However, the majority of the damage in civil engineering structures is nonlinear, such as fatigue cracks that open and close under dynamic loading. In this study, a new hybrid AR/ARCH model in the field of economics and a proposed damage indicator (DI) which is the second-order variance indicator (SOVI) based on the model have been used for detecting structural nonlinear damage. The data from an experimental three-storey structure and a simulated eight-storey shear building structure model have been used to verify the effectiveness of the algorithm and SOVI. In addition, a traditional linear DI: cepstral metric indicator (CMI) has also been used to diagnose the nonlinear damage. The results of the CMI and SOVI were compared and it is found that there are advantages in using the SOVI in the field of nonlinear structural damage.

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

[2]  Akira Mita,et al.  Localized Damage Detection of Structures Subject to Multiple Ambient Excitations Using Two Distance Measures for Autoregressive Models , 2009 .

[3]  Richard J. Martin.  A metric for ARMA processes , 2000, IEEE Trans. Signal Process..

[4]  Daniele Zonta,et al.  ARX residuals in damage detection , 2012 .

[5]  Hoon Sohn,et al.  A Bayesian Probabilistic Approach for Structure Damage Detection , 1997 .

[6]  Bo-Suk Yang,et al.  Estimation and forecasting of machine health condition using ARMA/GARCH model , 2010 .

[7]  Heung-Fai Lam,et al.  Bayesian structural damage detection of steel towers using measured modal parameters , 2015 .

[8]  Charles R. Farrar,et al.  Structural health monitoring algorithm comparisons using standard data sets , 2009 .

[9]  Spilios D Fassois,et al.  Time-series methods for fault detection and identification in vibrating structures , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Ling Yu,et al.  Structural Nonlinear Damage Identification Algorithm Based on Time Series ARMA/GARCH Model , 2013 .

[11]  Akira Mita,et al.  Damage indicator defined as the distance between ARMA models for structural health monitoring , 2008 .

[12]  Hojjat Adeli,et al.  Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures , 2016 .

[13]  R. Tsay.  Analysis of Financial Time Series: Tsay/Financial Time Series 3E , 2010 .

[14]  Marc Rébillat,et al.  Nonlinear structural damage detection based on cascade of Hammerstein models , 2014 .

[15]  Ling Yu,et al.  Nonlinear damage detection using higher statistical moments of structural responses , 2015 .

[16]  Akira Mita,et al.  Locating the damaged storey of a building using distance measures of low-order AR models , 2010 .

[17]  Ling Yu,et al.  Structural Damage Detection of Truss Bridge under Environmental Variability , 2015 .

[18]  Jun Zhang,et al.  Probability-Based Structural Health Monitoring Through Markov Chain Monte Carlo Sampling , 2016 .

[19]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[20]  Charles R. Farrar,et al.  Structural Health Monitoring Using Statistical Pattern Recognition Techniques , 2001 .

[21]  Vicente Lopes Junior,et al.  Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition , 2007 .

[22]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .