LAUSR

Scour is one of the primary reasons for the collapse of bridges, as it severely reduces the stability of their piers. This study developed a model updating method based on an improved genetic algorithm (GA) to identify scour depth, combined with a parallel computing technique to accelerate the model updating procedure. The proposed GA adopted a gradient-like calculation as the mutation operation, while the population in GA was initialized based on the exponential distribution. A finite element model of a continuous girder bridge, whose soil-structure interaction was simulated by winkle beam theory, was established to give a sensitivity analysis of the scour effect. A moving mass-spring model was used to simulate the vehicle-bridge interaction, and dynamic responses under various scour damages were generated to verify the method’s performance. The results of the numerical simulation indicated that the transverse vibration modes of a continuous girder bridge were sensitive to the scour effect, and the proposed method can identify the scour depth with an error of less than 0.31 m.