Damage Detection of a Continuous Bridge from Response of a Moving Vehicle

Abstract

This paper presents a multi-stage multi-pass method to identify the location of damage of a continuous bridge from the vertical response of a moving vehicle. The damage can be detected from this response by some existing methods if the road surface of the bridge is smooth, which implies that the vibration of the vehicle contains information of the damage. The detection becomes difficult if the road surface of the bridge is rough as it will affect the response of the vehicle and may mask the information of the damage. To deal with this problem, a multi-stage multi-pass method is proposed. A vehicle runs over the bridge several times at different velocities and the corresponding responses of the vehicle can be obtained. The vertical acceleration records of the vehicle running on the intact and damaged bridges are used for the identification. The multi-stage damage detection method is implemented by application of the modal strain energy based method, followed by a modified genetic algorithm. The modal strain energy method extracts frequencies from the responses of both the intact and damaged states of the bridge before calculation of a damage indicator which can be used to roughly estimate the location of damage. At the second stage, the identification problem is transformed into a global optimization problem and is solved by the genetic algorithm techniques. For each pass of the vehicle, the method can narrow down the possible locations of the damage until it is determined with acceptable accuracy. A two-span continuous bridge is used to verify the method. The bridge is modeled using the finite element method while the vehicle is modeled as a spring-mass-damper system. The damage is inflicted by reducing the stiffness of an element. In the numerical simulation, the vertical acceleration of the vehicle is computed using a vehicle-bridge-interaction system. The results show that this method can identify the location of damage reasonably well.