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.

Wind tunnel tests are carried out for the Commonwealth Advisory Aeronautical Research Council (CAARC) high-rise building with a scale of 1:400 in exposure categories D. The distribution law of extreme pressure coefficients under different conditions is studied. Probability distribution fitting is performed on the measured area-averaged extreme pressure coefficients. The general extreme value (GEV) distribution is preferred for probability distribution fitting of extreme pressure coefficients. From the comparison between the area-averaged coefficients and the value from GB50009-2012, it is indicated that the wind load coefficients from GB50009-2012 may be non-conservative for the CAARC building. The area reduction effect on the extreme wind pressure is smaller than that on the mean wind pressure from the code. The recommended formula of the area reduction factor for the extreme pressure coefficient is proposed in this study. It is found that the mean and the coefficient of variation (COV) for the directionality factors are 0.85 and 0.04, respectively, when the orientation of the building is given. If the uniform distribution is given for the building’s orientation, the mean value of the directionality factors is 0.88, which is close to the directionality factor of 0.90 given in the Chinese specifications.