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Smart Structures and Systems
  Volume 11, Number 1, January 2013, pages 53-67

Particle relaxation method for structural parameters identification based on Monte Carlo Filter
Tadanobu Sato and Youhei Tanaka

    In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.
Key Words
    Monte Carlo Filter; particle filter; system identification; genetic algorithm; relaxation technique; large scale system; shaking table test; model structure
Tadanobu Sato : International Institute of Urban engineering, Southeast University, Nanjing, China
Youhei Tanaka : Minicipality of Nishinomiya, Nishinomiya, Japan

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