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Identification of flexible vehicle parameters on bridge using particle filter method
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 Title & Authors
Identification of flexible vehicle parameters on bridge using particle filter method
Talukdar, S.; Lalthlamuana, R.;
 Abstract
A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.
 Keywords
conditional probability;vehicle flexibility;forward solution;bridge-vehicle interaction dynamics;noise level;
 Language
English
 Cited by
1.
Estimation of gross weight, suspension stiffness and damping of a loaded truck from bridge measurements, Structure and Infrastructure Engineering, 2017, 13, 11, 1497  crossref(new windwow)
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