Abstract
Direct measurements of external forces acting on a structure are infeasible in many cases. The Augmented Kalman Filter (AKF) has several attractive features that can be utilized to solve the inverse problem of identifying applied forces, as it requires the dynamic model and the measured responses of structure at only a few locations. But, the AKF intrinsically suffers from numerical instabilities when accelerations, which are the most common response measurements in structural dynamics, are the only measured responses. Although displacement measurements can be used to overcome the instability issue, the absolute displacement measurements are challenging and expensive for full-scale dynamic structures. In this paper, a reliable model-based data fusion approach to reconstruct dynamic forces applied to structures using heterogeneous structural measurements (i.e., strains and accelerations) in combination with AKF is investigated. The way of incorporating multi-sensor measurements in the AKF is formulated. Then the formulation is implemented and validated through numerical examples considering possible uncertainties in numerical modeling and sensor measurement. A planar truss example was chosen to clearly explain the formulation, while the method and formulation are applicable to other structures as well.
Highlights
Many engineering structures are subjected to various natural and man-made dynamic loads, including wind, earthquake, traffic, machine vibrations, and tidal loads
A multi-metric approach is investigated to improve the stability and accuracy of the force estimation using the Augmented Kalman Filter (AKF) method. It is shown how the stability issue of the AKF can be addressed and force estimation accuracy in both low- and high-frequency range can be enhanced by combined use of multi-metric measurements, i.e., strain and acceleration responses measured at limited locations of the structure, in the measurement update stage of the Kalman filter
Civil structures are exposed to both low- and high-frequency force excitations; the Multi-metric approach investigated here can improve the AKF accuracy for estimating broadband force excitations
Summary
Many engineering structures are subjected to various natural and man-made dynamic loads, including wind, earthquake, traffic, machine vibrations, and tidal loads. A Kalman Filter approach in conjunction with a recursive least-square algorithm has been developed for force excitation estimations [6,25] This method requires data vectors that contain displacement measurements at. A multi-metric approach is investigated to improve the stability and accuracy of the force estimation using the AKF method It is shown how the stability issue of the AKF can be addressed and force estimation accuracy in both low- and high-frequency range can be enhanced by combined use of multi-metric measurements, i.e., strain and acceleration responses measured at limited locations of the structure, in the measurement update stage of the Kalman filter.
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