Peak factor estimation of non‐Gaussian wind pressure on high‐rise buildings
Peak factor estimation of non‐Gaussian wind pressure on high‐rise buildings
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DOI：
10.1002/tal.1386
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Xingliang Ma;Fuyou Xu
中科院分区：
文献类型：
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作者：
Xingliang Ma;Fuyou Xu
大量的风对建筑物的非高斯效应的测量要求对非高斯过程进行更先进的极值估计方法的蓬勃发展。在这项研究中，提出了一种用于估计非高斯过程的峰值因子和建模极值分布的良好方法。该方法的特点是使用两个拟合的父非高斯过程的概率分布，分别实现长尾和短尾侧的极值估计。该方法采用约翰逊变换作为概率模型来拟合非高斯过程的母体分布，因为它具有上级拟合优度和普适性。对于每个数据集，将通过两种参数估计方法建立两个约翰逊变换，以分别估计两侧的极值。然后应用Gumbel假设以方便地确定非高斯峰值因子。该方法通过高层建筑模型表面的风洞试验实测风压记录进行了验证。结果表明，该方法是更准确和更强大的比许多现有的估计峰值因子的非高斯风压。
A vast quantity of measurements of wind‐induced non‐Gaussian effects on buildings call for the burgeoning development of more advanced extrema estimation approaches for non‐Gaussian processes. In this study, a well‐directed method for estimating the peak factor and modeling the extrema distribution for non‐Gaussian processes is proposed. This method is characterized by using two fitted probability distributions of the parent non‐Gaussian process to separately fulfill the estimations of the extrema on long‐tail and short‐tail sides. In this method, the Johnson transformation is adopted to be the probabilistic model for fitting the parent distribution of the non‐Gaussian process due to its superior fitting goodness and universality. For each dataset, two Johnson transformations will be established by two parameter estimation methods to individually estimate the extrema on two sides. Then a Gumbel assumption is applied for conveniently determining the non‐Gaussian peak factor. This method is validated through long‐duration wind pressure records measured on the model surfaces of a high‐rise building in wind tunnel test. The results show that the proposed method is more accurate and robust than many existing ones in estimating peak factors for non‐Gaussian wind pressures.