Abstract
The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.
Highlights
With the progress of the era, transportation and automobile industries have achieved rapid development, while vehicle loads acting on bridge structures are increased continuously
Vehicle loads of long-span bridges are significantly different from those of middle-span and small-span bridges, which are mainly reflected in the following aspects: main beams of long-span bridges have low rigidity, and the main beam deformation is obvious under vehicle loads; longspan bridges are mainly located at traffic throat positions with large traffic flow; long-span bridges are obviously affected by intensive vehicles
Zhang et al [11] solved random excitation caused by bridge deck roughness and certain excitation caused by gravity using a virtual excitation method; mean values and standard deviations of bridge midspan deflection and stress responses were obtained
Summary
With the progress of the era, transportation and automobile industries have achieved rapid development, while vehicle loads acting on bridge structures are increased continuously. Researches on random traffic flow loads acting on bridges are mainly focused on statistical analysis theories, depend too much on basic assumption about unchanging of vehicle type, vehicle weight, vehicle distance, and vehicle speed, and fail to fully study and consider random characteristics of each traffic flow parameters. Aiming at such situation, this paper tests bridge health and monitors traffic flow parameters including vehicle type, vehicle weight, and vehicle speed. The vehicle-bridge coupling vibration model with considering random traffic flows established in the paper is feasible
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