Bayesian parameter identification in dynamic state space models using modified measurement equations |
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| Affiliation: | 1. Department of Geotechnical Engineering, Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai, China;1. Structural Engineering Department, Federal University of Juiz de Fora, CEP 36036-330 Juiz de Fora, MG, Brazil;2. ISISE, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal;1. Servicio de Reumatología, Hospital Universitario de Salamanca, Salamanca, España;2. Instituto de Investigación Biomédica de Salamanca, Salamanca, España (IBSAL);1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, PR China;2. State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, PR China;1. National School of Engineers of Tunis (ENIT), Applied Mechanics and Engineering Laboratory, University of Tunis El Manar, BP 37,1002 Belvédère, Tunis, Tunisia;2. Preparatory Engineering Institute of Nabeul (IPEIN), Research Unit of Structural Dynamics, Modeling and Engineering of Multi-Physics Systems, University of Carthage, 8000 M׳rezgua, Nabeul, Tunisia;3. FEMTO-ST Institute UMR 6174, Department of Applied Mechanics, University of Franche-Comté, 24 Chemin de L׳Epitaphe, 25000 Besançon, France |
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| Abstract: | ![]()
When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending–torsion coupled, geometrically non-linear building frame under earthquake support motions. |
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| Keywords: | Bayesian method MCMC simulations Instrumented structures Non-linear system identification Vehicle structure interaction |
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