Dynamic Response Analysis of Long-Span Bridges under Random Traffic Flow Based on Sieving Method

To overcome the limitations of using time interval division to calculate the bridge impact coefficient (IM), a sieving method has been proposed. This method employs multiple sieves on bridge time–history curve samples to ultimately obtain the bridge impact coefficients. Firstly, CA cellular automata are used to establish different levels of traffic flow fleet models. The random traffic flow–bridge coupling dynamic model is established through wheel–bridge displacement coordination and mechanical coupling relationships based on the theory of modal synthesis. Then, the variation of bridge dynamic time–history curves for different classes of random traffic flow, speed and pavement unevenness parameters are analyzed. The sieving method is applied to screen the extreme points of the dynamic time–history curve of the bridge, enabling the distribution law of the bridge IM to be obtained using the Kolmogorov–Smirnov test (K–S test) and statistical analysis. Finally, the calculated value is then compared with the IM specifications of multiple countries. The results show that the proposed method has high identification accuracy and produces a good inspection effect. The value obtained using the sieving method is slightly larger than the value specified in the US code, 0.33, which is considerably larger than the values specified in other national codes. As pavement conditions deteriorate, the IM of the bridge increases rapidly, especially under Class C and Class D pavement unevenness, which exceed the values specified in various national bridge specifications.