Dr. Zhaoshuo Jiang Profile

Dr. Zhaoshuo Jiang

Assistant Professor at San Francisco State Univ

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Proceedings Article | 20 April 2016 Presentation + Paper

Extended Kalman filter based structural damage detection for MR damper controlled structures

Chenhao Jin, Shinae Jang, Xiaorong Sun, Zhaoshuo Jiang, Richard Christenson

Proceedings Volume 9803, 98031T (2016) https://doi.org/10.1117/12.2218934

KEYWORDS: Damage detection, Filtering (signal processing), Buildings, Earthquakes, System identification, Structural health monitoring, Control systems, Structural dynamics, Mathematical modeling, Dynamical systems

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The Magneto-rheological (MR) dampers have been widely used in many building and bridge structures against earthquake and wind loadings due to its advantages including mechanical simplicity, high dynamic range, low power requirements, large force capacity, and robustness. However, research about structural damage detection methods for MR damper controlled structures is limited. This paper aims to develop a real-time structural damage detection method for MR damper controlled structures. A novel state space model of MR damper controlled structure is first built by combining the structure’s equation of motion and MR damper’s hyperbolic tangent model. In this way, the state parameters of both the structure and MR damper are added in the state vector of the state space model. Extended Kalman filter is then used to provide prediction for state variables from measurement data. The two techniques are synergistically combined to identify parameters and track the changes of both structure and MR damper in real time. The proposed method is tested using response data of a three-floor MR damper controlled linear building structure under earthquake excitation. The testing results show that the adaptive extended Kalman filter based approach is capable to estimate not only structural parameters such as stiffness and damping of each floor, but also the parameters of MR damper, so that more insights and understanding of the damage can be obtained. The developed method also demonstrates high damage detection accuracy and light computation, as well as the potential to implement in a structural health monitoring system.

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