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Structural Nonlinear Damage Detection Based on Time Series Model and Probability Theory

  • Huiyong GuoEmail author
  • Feng Zhang
  • Jinjun Cheng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Damage of practical engineering structure usually possesses nonlinear feature and the results of damage diagnosis are also usually disturbed by different kinds of uncertainty, such as measurement noise and model error. Nonlinear and uncertain characteristics common in measurement signals bring challenges to damage detection. A novel method combined the probability theory with hybrid AR/ARCH model is proposed to detect structural nonlinear damage that couples with measurement uncertainty and structure uncertainty. The AR/ARCH model can fit structural acceleration response time series to effectively extract the nonlinear feature and the calculation probability of damage existence. Probability theory can deal with uncertainty caused by measurement noise or model errors. Therefore, the method based on AR/ARCH model and probability theory can improve the reliability of damage detection compared with deterministic method under different uncertainties. The results of a simulated 5-storey shear structure show the excellent performance to locate nonlinear damage and the potential to quantify the nonlinear damage degree.

Keywords

Nonlinear damage Time series model Probability theory Uncertainty Acceleration response 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51578094).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Civil EngineeringChongqing UniversityChongqingPeople’s Republic of China

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