Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2682
Title: Uncertainty Quantification for Structural Health Monitoring Applications
Authors: Nasr, Dana 
Slika, Wael G.
Saad, George A.
Affiliations: Department of Civil and Environmental Engineering 
Keywords: Uncertainty Quantification
Sequential data assimilation
Ensemble Kalman filter
Polynomial chaos Kalman filter{09}
Subjects: Systems identification
Issue Date: 2018
Part of: Journal of smart structures and systems
Volume: 22
Issue: 4
Start page: 399
End page: 411
Abstract: 
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.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2682
Type: Journal Article
Appears in Collections:Department of Civil and Environmental Engineering

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