In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction.

It is inevitably to encounter overtaking events as vehicles pass through long-span bridges under crosswinds. The effects of the aerodynamic interference on the dynamic responses of the overtaking vehicles should be taken into account in the analysis of the driving safety of vehicles under crosswinds. The time-varying aerodynamic coefficients of the overtaking vehicles are obtained by scale model wind tunnel tests. A vehicle dynamic model is established for the analysis of the dynamic responses of articulated lorry. Based on the coupling vibration framework of the wind-vehicle-bridge system, an improved dynamic analysis system is constructed to solve the transient aerodynamic responses of the overtaking vehicle. It investigates the effects of the overtaking events on the driving safety of vehicles on bridge deck under crosswinds. The results show that the interference aerodynamic forces produced during the overtaking process have significant influences on the dynamic responses of vehicles. The maximum load transfer ratio and side-slip factor of vehicles in consideration of the effects of overtaking event are much greater than the case taking no account of the interference aerodynamic forces. Although the overtaking event do not play a decisive role in the driving safety of vehicles crossing the bridge, it is necessary to consider the combined adverse effects of the overtaking event and crosswinds on the safety of vehicles.

The aerodynamic characteristics of road vehicles in windy environments are the prerequisites for the evaluation and prediction of the driving safety and stability. To investigate the aerodynamic characteristics of the overtaking vehicles on the bridge deck under the effects of crosswinds, models of a cable‐stayed bridge with a typical flat box girder and road vehicles involving articulated lorry and commercial van with a scale of 1 : 40 were tested in the wind tunnel laboratory. A series of tests figured out the variation of the aerodynamic forces of road vehicles during the overtaking process after considering the aerodynamic interference between lorry and lorry, van and van, and lorry and van. Additionally, the influence of the lateral overtaking distance between the overtaking vehicles was regarded as well. The result reveals the upstream vehicle has a significant influence on the aerodynamic coefficients of the downstream vehicle, which have experienced dramatic fluctuations during the overtaking process, and the various shapes of the aerodynamic coefficients are highly dependent on it.

The pseudoexcitation method (PEM) can improve efficiency of random vibration analysis. However, for large‐sized structures with wide frequency range of response, the workload of calculation is heavy if conventional integration methods, such as trapezoidal integration, are used to combine with the PEM to calculate structural response. In such case, self‐adaptive technology is induced to combine with the PEM to form an efficient method for solving random vibration. During calculation, this method can realize the adaptability of random excitation to actual structural response, identify automatically critical frequency intervals of random excitation, and process intelligently the identified critical frequency intervals and noncritical frequency intervals. Based on the identified frequency intervals, Gauss integration is carried out to obtain response results with random characteristics. The computational efficiency and accuracy of PEM‐SGI are verified by wind‐induced performance of the slender bridge tower. Finally, the influence of damping ratio of the bridge structure and train marshalling on vehicle‐bridge coupled system is investigated to further verify the application of the proposed method. Results show that the efficiency of solving random vibration can be improved by the present method.