Abstract Due to the fickle distribution characteristics, good approximation of the wind pressure probability density function for an entire roof is challenging, especially for tail region. Maximum entropy model (MEM) can theoretically generate the least biased distribution. However, classical MEM may lead to unreliable estimates of the corresponding integer moments. This study developed a new maximum entropy model with fractional moments. There are several important features: A new translation function is introduced, thus allowing negative data to be modeled. For a good bias-variance trade-off, the constraint number M was fixed as four. After estimating the initial value of the Lagrange multiplier λ by a computationally simple linear equation system, a simple search was carried out to find a better solution. The generalized pattern search was adopted for determining the global optimal solution for the fractional moment orders α . The performance was benchmarked through typical field measurement data of wind pressures on the roof of a low-rise building under typhoon conditions. Compared with common models, the proposed method had better performance and more stable results for the dataset examined for the whole roof. This method is also beneficial for peak value evaluation and the simulation of wind pressures.

中文翻译：

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低层建筑风压概率密度函数估计的分数阶最大熵模型

摘要 由于分布特征变化无常，对整个屋顶的风压概率密度函数的良好逼近具有挑战性，尤其是尾部区域。最大熵模型 (MEM) 理论上可以生成最小偏差分布。然而，经典的 MEM 可能会导致对相应整数矩的不可靠估计。本研究开发了一种新的具有分数矩的最大熵模型。有几个重要的特点： 引入了新的翻译功能，从而允许对负面数据进行建模。为了获得良好的偏差-方差权衡，约束数 M 固定为 4。通过计算简单的线性方程组估计拉格朗日乘子 λ 的初始值后，进行简单搜索以找到更好的解。采用广义模式搜索来确定分数阶矩 α 的全局最优解。该性能通过台风条件下低层建筑屋顶风压的典型现场测量数据进行基准测试。与普通模型相比，所提出的方法对整个屋顶检查的数据集具有更好的性能和更稳定的结果。该方法也有利于峰值评估和风压模拟。对于整个屋顶检查的数据集，所提出的方法具有更好的性能和更稳定的结果。该方法也有利于峰值评估和风压模拟。对于整个屋顶检查的数据集，所提出的方法具有更好的性能和更稳定的结果。该方法也有利于峰值评估和风压模拟。