Abstract
Wind-induced pressures on high-rise buildings claddings are mostly non-Gaussian distribution, and there is a one-to-one relationship between a specified guarantee rate and its corresponding peak factor. In this study, a stepwise search method for calculating the peak factor of non-Gaussian wind pressure and a gradual independent segmentation method for extracting independent peak values are proposed to determine the relationship accurately. Based on the pressure data of a high-rise building obtained from a rigid model wind tunnel test, the peak factors of non-Gaussian wind pressures on claddings are calculated and compared by using several typical methods. The value of the peak factor and its error rate calculated by several methods is compared with the observed average peak value, and the conversion between the guaranteed rate and the peak factor is discussed. Based on the reliability theory, the true distribution of wind pressure time history was approached infinitely through an efficient numerical method in the process of stepwise search. Compared with the classical Sadek–Simiu method, the proposed stepwise search method achieves improved overall accuracy and applicability. The non-Gaussian features are found to be prominent at leading edge airflow separation on the crosswind side, the leeward corner cuts, the windward corner cuts, and the junction of two leeward surfaces at 45° wind direction angle of square section. The junction of two leeward surfaces at 45° wind direction angle exhibits stronger non-Gaussian features than the crosswind surface at 0° wind direction angle. By giving the identical guarantee rate, the peak factors tend to be much larger in the regions with strong non-Gaussian properties and vice versa.
Highlights
Wind load is a decisive load in the design of high-rise buildings, especially for claddings, and most of the critical wind pressures on the building cladding are non-Gaussian signals. erefore, it is critical to determine the maximum wind pressures for design with an appropriate guarantee rate in the engineering design of high-rise building that considers both safety and economy.For the wind-resistant design of buildings claddings, the distribution of extreme wind pressure is primarily considered, by which the design wind load with a certain guarantee rate can be determined based on reliability theory [1, 2]. e object of extreme wind pressure estimation can be the parent distribution or the extracted extreme value distribution
Kareem and Zhao [3] expressed the non-Gaussian process as Hermite polynomials of the Gaussian process considering higher-order statistics, extending the peak factor method to the non-Gaussian process. e process conversion method of Sadek–Simiu [4] uses the principle of equivalent probability to transform non-Gaussian process into Gaussian process and proposes a method for calculating the extreme value of non-Gaussian process
The peak factors of wind pressures were calculated by the stepwise search method proposed in this study and by the other two approaches suggested by Kareem and Zhao and Sadek and Simiu
Summary
Wind load is a decisive load in the design of high-rise buildings, especially for claddings, and most of the critical wind pressures on the building cladding are non-Gaussian signals. erefore, it is critical to determine the maximum (minimum) wind pressures for design with an appropriate guarantee rate in the engineering design of high-rise building that considers both safety and economy.For the wind-resistant design of buildings claddings, the distribution of extreme wind pressure is primarily considered, by which the design wind load with a certain guarantee rate can be determined based on reliability theory [1, 2]. e object of extreme wind pressure estimation can be the parent distribution or the extracted extreme value distribution. Erefore, it is critical to determine the maximum (minimum) wind pressures for design with an appropriate guarantee rate in the engineering design of high-rise building that considers both safety and economy. When the estimation takes the extreme value distribution as an object, it will mainly discuss the distribution model of wind pressure extremum, the parameter estimation method, the extremum sample acquisition method, and so on. Based on the Davenport method for the Gaussian process, several scholars have proposed different improvements to the calculation model and determination method of the non-Gaussian peak factor of fluctuating wind pressure. For the extreme value sequence, the Method of Independent Storms (MIS) proposed by Cook [9] extracts the data segments whose wind speed (or wind pressure) continuously exceeds the threshold value as an independent storm and takes the peak value of each independent storm as the sample of extreme value analysis. Quan and Gu Ming [13] segmented a single standard sample and estimated the non-Gaussian wind pressure extreme value based on the classical extreme value theory
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