The Structural Nonlinear Damage Detection Based on Linear Time Series Algorithm

摘要

Two new algorithms for nonlinear damage detection are proposed based on linear model with autoregressive moving average (ARMA) in this paper. Firstly, a novel DSF is defined and the DSFs are identified and classified followed by cluster analysis or Bayesian discrimination. Secondly, the performances of the presented algorithms are evaluated and verified by the experimental data of a three-story building structure. Finally, the illustrated results show the algorithms are efficient tools for nonlinear damage detection. They grant a higher accuracy and improve the reliability of nonlinear damage detection whilst reducing computational costs. It can thus be inferred that the proposed algorithms are applicable for Structural Health Monitoring (SHM) in situ.