Parameter identification of vehicles moving on continuous bridges

This paper describes a method for the identification of parameters of vehicles moving on multi-span continuous bridges. Each moving vehicle is modelled as a 2-degree-of-freedom system that comprises four components: an unsprung mass and a sprung mass, which are connected together by a damper and a spring. The corresponding parameters of these four components, namely, the equivalent unsprung mass and sprung mass, the damping coefficient and the spring stiffness are identified based on dynamic simulation of the vehicle–bridge system. The identification process makes use of acceleration measurements at selected stations on the bridge. In the study, the acceleration measurements are simulated from the solution to the forward problem of a continuous beam under moving vehicles, together with the addition of artificially generated measurement noise. The identification is carried out through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the moving vehicles. This multi-stage optimization scheme reduces the variable search domains stage by stage using the identified results of the previous stage. Besides the basic operators in simple genetic algorithms, some advanced genetic operators and techniques are adopted here. Therefore, it makes the proposed identification procedure much more efficient than other traditional optimization methods. The identification procedure is then verified with a few test cases. The estimated vehicle parameters can also be used to get the time varying contact forces between the vehicles and bridge surface.