【摘要】 以经典极值理论为基础,发展了一种基于单个标准样本的非高斯风压的极值计算方法.首先介绍了风洞试验及试验数据处理的基本概况;然后在阐述经典Gumbel极值理论的基础上,根据观察极值的相互独立性推导了短时距下极值分布参数和长时距下极值分布参数的关系式,给出了一种由短时距样本推算长时距下的极值估算方法;最后基于风洞试验数据,将常用极值估算方法(峰值因子法、改进峰值因子法和Sadek-Simiu法)和该方法的计算结果进行了比较.得到如下结论:将一个标准长度的非高斯风压时程数据划分成若干等长的子段,可以通过子段的极值分布规律准确地估算出母段的极值期望值,子段的最佳分段长度可以通过自相关分析给出;与常用的极值估算方法相比,该方法更能准确估计非高斯风压的极值.更多还原

【Abstract】 Based on the classical extreme-value theory,an estimating method for expected extreme-value of wind pressure with non-Gaussian probability distribution is proposed with a sample whose length is just one standard observation interval.At first,the wind tunnel test and test data process of this study are introduced in detail.A method to calculate expected extreme values of time series with a long time interval is then proposed with its sub-sections based on the classical Gumbel theory for extreme values and the independence of observed extreme values.At last,the extreme values of the wind pressure coefficients of the present wind tunnel test are calculated with the proposed method and methods used widely at present,such as peak factor method, improved peak factor method and Sadek-Simiu method.The results indicate that the probatilistic parameters of extreme values of sub-sections of a non-Gaussian wind pressure time series can be used to estimate accurately expected extreme value of their parent section with the proposed method.The length of the sub-section can be determined with auto-correlation analysis on the parent section.Comparison shows that the proposed method can estimate extreme values of non-Gaussian wind pressure more accurately than other methods used widely at present.更多还原