Volume 4, Issue 3 (3-2020)                   NMCE 2020, 4(3): 42-58 | Back to browse issues page


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Ebrahimi M, Karami Mohammadi R, Sharafi F. The Particle Filter and Extended Kalman Filter methods for the structural system identification considering various uncertainties. NMCE 2020; 4 (3) :42-58
URL: http://nmce.kntu.ac.ir/article-1-267-en.html
1- Ph. D. Candidate, Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran.
2- Corresponding Author: Associate Professor, Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran. , rkarami@kntu.ac.ir
3- M.Sc. in Earthquake Engineering, Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Abstract:   (852 Views)
Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel structure subjected to the base excitation has been implemented using these methods by considering the modeling and material model uncertainties. Implementing the 2D and 3D modelings, using the “parallelogram” and “scissors” methods for the modeling of panel zones and that of the wall panels by two methods (using beam-column elements and equivalent diagonal strut elements), are the assumptions of this study. Using the parallelogram method has resulted in fewer errors in the 2D modeling while implementing different methods for simulation of wall panels has had no specific achievements. As illustrated in the results, more significant uncertainties were expected in systems with highly nonlinear behavior, since the equivalent linearization was used to estimate the system states in the EKF method. However, this method is less time-consuming and gives more accurate results in comparison with the PF method, in which a lrge number of samples are required for the system identification.
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Type of Study: Research | Subject: Special
Received: 2019/12/5 | Revised: 2020/01/5 | Accepted: 2020/02/5

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