Yuji Tian

The probability distributions of extreme wind speeds and wind pressure coefficients affect the probability distribution of extreme wind load effects. Typically, the probability distribution of extreme wind loads can be obtained using the full probability theory and first-order and full-order analysis methods. Using the Gumbel distribution model, the effects of the variabilities of extreme wind speeds and wind pressure coefficients on extreme wind load predictions are further analyzed. To explore the uncertainties of extreme wind load for low-rise buildings at a given guarantee rate (non-exceedance probability), based on the Gaussian assumption and the calculation principle of the first-order and full-order methods, an uncertainty analysis model of the extreme wind load at an arbitrary guarantee rate is established. On this basis, the effects of the extreme wind pressure coefficients variability on the prediction interval of extreme wind load are investigated. The results show that the variability of extreme wind speed significantly affects the distribution parameters and prediction results of extreme wind loads, whereas the variability of the extreme wind pressure coefficient has a relatively minor effect. In addition, taking a 57% guarantee rate as an example, when the coefficient of variation in the extreme wind pressure is less than 0.6, the estimated interval width of the extreme wind load increases with the coefficient of variation. Finally, the proposed uncertainty analysis model of extreme wind load provides a method for the performance-based wind-resistant design and analysis of low-rise buildings.

When extreme wind pressure is predicted based on the extreme value distribution theory, the sampling time–distance and sample volume of wind pressure data are important influencing factors. To discuss and analyze the influence of time–distance and sample volume on the prediction results and accuracy of extreme wind pressure, according to the wind tunnel pressure test results of low buildings, the extreme wind pressure results corresponding to different working conditions of time–distance and sample volume are calculated, and the prediction results are compared and analyzed. At the same time, the wind pressure conversion ratio variable is introduced to analyze the prediction accuracy of extreme wind pressure. According to the calculation results of the wind pressure conversion ratio under different working conditions, the empirical calculation formula of wind pressure conversion ratio concerning time–distance and sample volume is further established, and the applicability of the empirical calculation formula is verified and analyzed. The results show that a reasonable increase in time–distance and sample volume can effectively improve the prediction accuracy of extreme wind pressure. Meanwhile, the empirical calculation formula of the wind pressure conversion ratio provides a method for quantitative analysis of the impact of time–distance and sample volume change on the prediction accuracy of extreme wind pressure, and it is also a theoretical reference for the uncertainty analysis of extreme wind pressure.

To predict and explore the return period wind speed distribution in Beijing urban area, based on the short-term measured wind speed record, the measured independent storm peak samples are extracted and the Monte–Carlo numerical simulation method is used to randomly generate pseudo-independent storm peak samples with the same distribution as the independent storm peak samples. In addition, the appropriate threshold is used to select and extract the over-threshold independent storm peak samples from the measured samples. And, then, the return period wind speed of the whole wind direction is predicted. Further, the traditional Cook method is used to predict and analyze the return period wind speed of each wind direction according to the measured wind speed data. The results show that the return period wind speed prediction results of the whole wind direction corresponding to the simulated pseudo samples and the measured over-threshold samples are basically close to each other, and the maximum relative deviation is less than 7%. The mean relative deviation of the return period wind speed prediction results is about 10.6% between each wind direction and the whole wind direction, and the mean relative deviation is about 9.4% between the RPWS prediction result of each wind direction and the design value of the code. In addition, the return period wind speed prediction values of the west by southwest, northwest and north by northeast wind directions exceed the design value of the code, while the predicted values of the other wind directions are within the envelope range of the design value of the code. Finally, the research content of this paper can provide some reference for the wind resistance design analysis of building structures in Beijing area.

The risk of trains being hit by tornadoes in China continues to increase due to the increasing density of railway lines and the shortening of the train departure intervals and the increasing probability of extreme weather phenomena caused by global climate change. If a train is hit by a tornado, it will cause huge casualties and economic losses, so it is necessary to investigate the tornado-induced effects on trains. A series of rigid-model wind pressure measurement tests on a train car under tornado wind loading were conducted using a tornado-vortex simulator, in order to determine the effects of the distance between the train car and the tornado’s center, the swirl ratio of a tornado-like vortex, and the ground roughness on the wind pressure distributions and wind load characteristics on trains. Apparent discrepancies were observed between tornado-induced wind loading and lateral wind loading obtained from conventional boundary-layer wind tunnel tests. The wind pressure and wind load on the car surface are mainly affected by the combined effects of the aerodynamic flow-structure interaction and the pressure drop accompanying the tornado within 1.5 times the vortex core’s radius, and the impact of tornado-like vortices on the train car is almost negligible as the distance from the train car to the tornado’s center exceeds three times vortex core’s radius. The variation trend of mean/fluctuating pressure coefficients is generally consistent. Large values of fluctuating pressure exist mainly on the top and side surfaces of the train car, especially the side surface proximal to the tornado’s center. The most unfavorable mean sectional side force coefficients were found when the train car is located in the tornado’s core and the largest lift force coefficients at the tornado’s center. The overall side force coefficients peaked when the train car is located at a distance of 1.5 times the tornado’s core radius, whereas the largest lift force coefficients were found when the train car was located at the tornado’s center. The overall distribution patterns of the wind force coefficients of the car under different swirl ratios and ground roughness levels are basically the same. The peak aerodynamic force value increases with increasing swirl ratio, and it decreases as ground roughness increases.

The low-aspect-ratio cylindrical structures represented by oil tanks is a kind of wind sensitive structure, which is prone to buckling under wind-loads. A wind tunnel test was conducted to investigate the properties of wind-loads acting on smooth cylinders with an aspect ratio AR = 0.323 and 0.875, respectively. Some parameters, such as Reynolds number (Re) and turbulence intensity, were taken into account. The results reveal that low-aspect-ratio cylinders have a Re effect, and the effect rises with AR. AR is the main factor affecting the value of the base pressure coefficient and positive pressure range, and the former increases with AR, while the latter decreases with AR. Moreover, due to the influence of the free end and turbulence, which may suppress vortex shedding, the power spectrum of the lift coefficient essentially shows broad spectral peaks with Re and turbulence. Increasing the incoming turbulence made the flow round cylinders at a higher Re state, that is, the supercritical regime is reached at a smaller Re. When turbulence is greater than 4.0%, turbulence and Re have little effect on the mean base force coefficient of low-aspect-ratio cylinders.

Under the action of the same wind azimuth, the extreme values of the wind load effect components of building structures are generated in the along-wind, cross-wind, vertical, and torsional directions. In designing the wind-resistant structure, the extreme values of effect components need to be combined to determine the internal force envelope values of members. Complete quadratic combination (CQC) and Turkstra combination rules are often used to determine the combination value of extreme values of wind effect components. The extreme probability distribution expressions of the CQC, and the Turkstra and approximate rules, are derived. The simplified combination Equations and combination coefficients of the CQC and Turkstra approximate rules are proposed in this paper. We use the combination Equations and Monte Carlo simulation method to analyze the accuracy of Turkstra and its approximate rules. The results show that the combination extreme is associated with the correlation coefficients, mean values, ratios of standard deviations, and fluctuating extremes of effect components. The errors between Turkstra and its approximate rules are small when load effect components show a positive correlation. The errors are largest when the standard deviations of components are equal. Our research results provide a theoretical basis for the combination method of wind load effect components of building structures.