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.
Chenhao Jin, Shinae Jang, Xiaorong Sun, Zhaoshuo Jiang, and Richard Christenson, "Extended Kalman filter based structural damage detection for MR damper controlled structures," Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 98031T (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 23, 2016; Published: 20 April 2016); https://doi.org/10.1117/12.2218934.
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