Abstract Observing wind loads directly in model scale is an effective way to predict extreme loads from the tail of the wind pressure distribution, but to ensure the reliability of the predictions, long-term data may be necessary. Such data are expensive in a wind tunnel. In this study, a criterion is established for dividing a short-term wind pressure sample into independent sub-samples. The observation duration of the sub-samples needed is derived based on mutual information theory. A modified observed extreme method for estimating extreme wind pressure from a short-term sample is proposed which combines an observed extreme method with an extreme value conversion relationship based on the Gumbel distribution. The proposed method is demonstrated by estimating the extreme wind pressures on a flat long-span roof from model-scale test results. The analysis shows that the test data can be segmented independently using the mutual information method. The delay time distribution is revealed to be highly correlated with the turbulence acting on the roof. The compensation value in the extreme value conversion relationship is shown to strongly influence the estimation result. Compared with the commonly-used methods for estimating extreme values, the proposed method has the advantage of more stable and effective statistical results.

中文翻译：

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估计大跨度屋顶的极端风压：样本独立性考虑

摘要 直接在模型尺度上观测风荷载是从风压分布尾部预测极端荷载的有效方法，但为保证预测的可靠性，可能需要长期数据。这样的数据在风洞中是昂贵的。在本研究中，建立了将短期风压样本划分为独立子样本的标准。所需子样本的观察持续时间是基于互信息理论得出的。提出了一种改进的观测极值法，用于从短期样本估计极端风压，该方法将观测极值法与基于 Gumbel 分布的极值转换关系相结合。所提出的方法通过从模型规模的测试结果估计平坦的大跨度屋顶上的极端风压来证明。分析表明，使用互信息方法可以对测试数据进行独立分割。显示延迟时间分布与作用在屋顶上的湍流高度相关。极值转换关系中的补偿值显示出对估计结果的强烈影响。与常用的极值估计方法相比，该方法具有统计结果更加稳定有效的优点。