TIME SERIES BASED STRUCTURAL NONLINEAR DAMAGE IDENTIFICATION ALGORITHM USING ARMA/GARCH MODEL

摘要

A new algorithm for nonlinear damage detection is proposed based on a model of autoregressive moving average with generalized autoregressive conditional heteroscedasticity (ARMA/GARCH) in this paper.First,the reference ARMA model is constructed with the acceleration responses measured in reference (healthy) state.One-step-ahead error predictions are then modeled as GARCH models.Secondly,the new nonlinear damage-sensitive feature (DSF) is defined as the GARCH model conditional standard deviation of ARMA model residual error in the reference and unknown states,respectively.The performance of the presented algorithm is evaluated and verified by the experinaental data of a three-story building structure provided by Los Alamos National Laboratory (LANL) USA.Finally,the new algorithm is compared with the traditional methods based on the standard deviation ratio of the residual error of ARMA model.The illustrated results show that the proposed method can effectively estimate the extent of nonlinear damage with a higher accuracy,less computational cost and more robustness against operational and environmental variety.This makes the proposed algorithm applicable for structural health monitoring in situ.