Smart Structures and Systems

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Nonlinear damage detection using linear ARMA models with classification algorithms

  • Chen, Liujie (School of Civil Engineering, Guangzhou University) ;
  • Yu, Ling (MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University) ;
  • Fu, Jiyang (Guangzhou University-Tamkang University Joint Research Center for Engineering Structure Disasters Prevention and Control) ;
  • Ng, Ching-Tai (School of Civil, Environmental & Mining Engineering, The University of Adelaide)
  • Received : 2019.08.08
  • Accepted : 2020.04.20
  • Published : 2020.07.25
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Abstract

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

Keywords

Acknowledgement

The research described in this paper was financially supported by the open project foundation (Grant No. 20160626005) from MOE Key Lab of Disaster Forecast and Control in Engineering at Jinan University in China.

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