Vehicle Parameter Identification through Particle Filter using Bridge Responses and Estimated Profile

Abstract

The weight of vehicles driving over bridges needs to be evaluated because the vehicle load potentially causes problems such as fatigue and large vibration. Bridge Weigh-In-Motion systems to evaluate vehicle load by measuring strain response of bridges have been proposed. However, the installation of strain gauges at bridge members are often costly and time consuming, limiting practical applications. This paper investigates the identification of vehicle weight using bridge acceleration response data at different sensor locations through the numerical simulation of vehicle-bridge interaction system. The identified parameters are not limited to the vehicle weight; suspension stiffness and damping coefficients are also identified. A data assimilation technique known as particle filter based on the Bayesian theory is employed to identify the parameters. The time history of the profile is estimated using the same particle filter technique from the dynamic response of a probe vehicle equipped with sensors. The proposed method is shown to have robustness against noises for the mass parameter identification.