Smart Structures and Systems

  • 제22권4호
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  • Pages.399-411
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  • 2018
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Uncertainty quantification for structural health monitoring applications

  • Nasr, Dana E. (Department of Civil and Environmental Engineering, American University of Beirut) ;
  • Slika, Wael G. (Department of Civil and Environmental Engineering, American University of Beirut) ;
  • Saad, George A. (Department of Civil and Environmental Engineering, American University of Beirut)
  • 투고 : 2017.11.13
  • 심사 : 2018.09.03
  • 발행 : 2018.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

키워드

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