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Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution.

Cai Wu1, Shujin Li2, Yuanjin Zhang3

  • 1School of Civil Engineering and Architecture, Wuhan University of Technology, Luoshi Road No.122, Wuhan 430070, China. wucai@whut.edu.cn.

Sensors (Basel, Switzerland)
|October 11, 2019
PubMed
Summary

This study introduces a new total least-squares (TLS_E) method for structural damage identification, improving accuracy by accounting for observation errors in auto-regressive models. The method effectively identifies damage location and degree in structures.

Keywords:
auto-regressive modeldamage identificationtotal least-squares method

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Area of Science:

  • Structural Engineering
  • Vibration Analysis
  • Damage Detection

Background:

  • Structural aging and external excitations necessitate robust damage identification methods.
  • Auto-regressive (AR) models are common for structural damage identification.
  • Classical least-squares (LS) algorithms for AR models often neglect observation noise.

Purpose of the Study:

  • To develop a more accurate structural damage identification method by addressing limitations of classical approaches.
  • To introduce a partial errors-in-variables (EIV) model that considers both current and prior observation errors.
  • To validate the proposed method's effectiveness in identifying structural damage location and degree.

Main Methods:

  • A partial errors-in-variables (EIV) model was formulated to incorporate observation errors.
  • A total least-squares (TLS_E) solution was developed to solve the partial EIV model.
  • An effective damage indicator was employed to quantify damage levels.

Main Results:

  • The proposed TLS_E method demonstrated superior accuracy compared to classical LS and AR models in simulations.
  • The method effectively estimates and accounts for correlations between observed data and the design matrix.
  • Validation using high-rise building shaking table test data confirmed the method's practical applicability.

Conclusions:

  • The TLS_E method offers a significant improvement in accuracy for structural damage identification.
  • This approach provides a more robust solution by accounting for noise in observed data.
  • The study successfully validated the method's capability in pinpointing damage location and severity in real-world structures.