Simultaneous Assessment of Damage and Unknown Input for Large Structural Systems by UKF-UI
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VIEW THE REPLYPublication: Journal of Engineering Mechanics
Volume 147, Issue 10
Abstract
Much progress has been made in the assessment of structural damage and unknown input (UI) using incomplete and noisy measurement signals. The unscented Kalman filter (UKF) has proved to be a sophisticated approach to this task. A novel method using UKF with unknown input (UKF-UI) for recursive identification of a state-input system has been proposed by the authors. However, the purpose of this study was to propose the new UKF-UI framework and validate it with some simple structures. Although very limited research has been conducted on the UKF for health assessment of large structural systems, including two-dimensional (2D) and three-dimensional (3D) frame structures, it is based on a two-stage approach and requires full measurement of all acceleration, velocity, and displacement responses in the substructure containing the UI. Some implementations either have limitations in real-time identification or need assumptions on the time evolution of UI. One example is the random walk hypothesis, which heavily depends on the tuning of noise parameters. The application of UKF to large structural systems is still a challenging problem. This observation has prompted the authors to investigate the UKF-UI framework for identification of large structural systems. Here, it is extended to the assessment of damage and UI by the UKF-UI method for 2D and a 3D finite-element (FE) frame models. By the partially measured noise-polluted structural acceleration and displacement responses, the extent and location of damage is assessed at the element level. The unknown external excitations are simultaneously identified with no assumptions about the time evolutions of a one-stage identification process.
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Data Availability Statement
All data, models, and code generated or used during the study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is supported by the National Key R&D Program of China through Grant No. 2017YFC1500603 and by the National Natural Science Foundation of China (NSFC) through Grant No. 51678509.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 3, 2020
Accepted: May 5, 2021
Published online: Aug 12, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 12, 2022
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