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On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework

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  • Datteo, Alessio
  • Busca, Giorgio
  • Quattromani, Gianluca
  • Cigada, Alfredo

Abstract

This paper proposes a complete sensitivity analysis of the use of Autoregressive models (AR) and Mahalanobis Squared Distance in the field of Structural Health Monitoring (SHM). Autoregressive models come from econometrics and their use for modelling the response of a physical system has been well established in the last twenty years. However, their aware application in engineering should be supported by knowledge about how they describe phenomena which are well defined by physics. Since autoregressive models are estimated by a least square minimization, statistical tools like Global Sensitivity Analysis and uncertainty propagation are powerful methods to investigate the performance of AR models applied to SHM.

Suggested Citation

  • Datteo, Alessio & Busca, Giorgio & Quattromani, Gianluca & Cigada, Alfredo, 2018. "On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 99-115.
    • Handle: RePEc:eee:reensy:v:170:y:2018:i:c:p:99-115
    DOI: 10.1016/j.ress.2017.10.017
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    References listed on IDEAS

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    5. Wei, Pengfei & Lu, Zhenzhou & Ruan, Wenbin & Song, Jingwen, 2014. "Regional sensitivity analysis using revised mean and variance ratio functions," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 121-135.
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    Cited by:

    1. Xin Wang & Yi Zhuo & Shunlong Li, 2023. "Damage Detection of High-Speed Railway Box Girder Using Train-Induced Dynamic Responses," Sustainability, MDPI, vol. 15(11), pages 1-19, May.

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