Smart Structures and Systems

  • 제27권2호
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  • Pages.335-350
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  • 2021
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

On the use of multivariate autoregressive models for vibration-based damage detection and localization

  • Achilli, Alessandra (Department DICAM, University of Bologna) ;
  • Bernagozzi, Giacomo (Department DICAM, University of Bologna) ;
  • Betti, Raimondo (Department of Civil Engineering and Engineering Mechanics, Columbia University) ;
  • Diotallevi, Pier Paolo (Department DICAM, University of Bologna) ;
  • Landi, Luca (Department DICAM, University of Bologna) ;
  • Quqa, Said (Department DICAM, University of Bologna) ;
  • Tronci, Eleonora M. (Department of Civil Engineering and Engineering Mechanics, Columbia University)
  • 투고 : 2020.07.07
  • 심사 : 2020.10.03
  • 발행 : 2021.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a novel method suitable for vibration-based damage identification of civil structures and infrastructures under ambient excitation. The damage-sensitive feature employed in the presented algorithm consists of a vector of multivariate autoregressive parameters estimated from the vibration responses collected at different locations of the analyzed structure. Outlier analysis and statistical pattern recognition are exploited for damage detection and localization. In particular, the Mahalanobis distance between a set of reference (i.e., "healthy") and inspection parameters is evaluated. A threshold is then selected to determine whether the inspection vectors refer to damaged or undamaged conditions. The effectiveness of the proposed approach is proved using numerical simulations and experimental data from a benchmark test. The analysis results show that the largest values of Mahalanobis distance can be found in the proximity of those sensors closest to the damaged elements. Thus, the Mahalanobis distance applied to vectors of multivariate autoregressive parameters has proven to be a robust indicator for damage detection and localization.

키워드

과제정보

The authors would like to acknowledge the Engineering Institute at Los Alamos National Laboratory for making available to the public domain the experimental data used in this work, and downloadable from http://institute.lanl.gov/ei/software-and-data/.

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