Intelligent Prediction of Buffeting Responses of Long-span Bridge Under the Action of Thunderstorm Winds

An intelligent prediction of the buffeting responses of a long-span bridge under thunderstorm winds was performed for real-time extrapolation of bridge wind-induced vibrations in an exceptional wind environment. Based on the actual measured data of the Sutong Bridge, the correlation between the wind field parameters and the buffeting responses of the main girder was analyzed, and the main parameters related to the thunderstorm wind effects were determined. Adopting the main wind parameters and historical buffeting responses as input parameters, a prediction network was constructed and trained based on typical artificial neural network models involving feedforward neural networks (FNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs). Finally, the predictive effects of the four models were compared and analyzed. The analytical results indicate that the thunderstorm wind-induced buffeting responses of the long-span bridge arc primarily related to the mean wind speed, mean wind direction, root mean square of the fluctuating wind speeds, and turbulence integral scale. The responses to be predicted depend on the historical wind field parameters and bridge motions, which necessitate the consideration of the memory effect of these two factors. The FNN and CNN fail to capture the memory effect well; therefore, the predicted results arc only similar to the measured values in the trend, and the prediction errors arc relatively large. The prediction effects of the GRU and LSTM are generally better, and the GRU provides the best prediction when the wind speed is high. At high wind speeds, the prediction effect of the LSTM is slightly weaker than that of the GRU. However, a higher prediction accuracy of the buffeting responses is obtained by the LSTM at a low wind speed, demonstrating better generalization of the approach. These results can provide a reference for the safe operation and maintenance of long-span bridges in thunderstorm and wind-prone areas. © 2023 Xi'an Highway University. All rights reserved.