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Development of probabilistic assessment framework for pedestrian wind environment using Bayesian technique

机译：使用贝叶斯技术开发步行风环境的概率评估框架

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摘要

This study extends the assessment framework proposed by Murakami et al. (1986) for the pedestrian wind environment to a fully probabilistic method by implementing Bayesian modeling. The method quantifies the uncertainties in constructed models based on the measured wind data and the results of the assessment. To model the probabilities of the daily maximum mean wind speed and wind direction, we employed the Weibull distribution and categorical distribution, respectively. The parameters defining the probability distributions were probabilistically modeled using Bayesian techniques. Using the wind data measured at the Meteorological Observatory of Tokyo, we demonstrated the effectiveness of the proposed method. The results showed that the wind direction probability and each parameter of the Weibull distribution could be estimated in the form of a posterior probability density function. Using the constructed models, we predicted the exceedance probability of the daily maximum instantaneous wind speed and evaluated the wind environment index (rank) in a city model. We provided a discrete rank scale in the form of a probability distribution, which enables us to quantify the evaluation uncertainties. Additionally, we clarified the effect of varying the amount of data used for the model construction. The uncertainty of the exceedance probability decreased with the amount of data. When only 1 year of data was used, some evaluation points possibly changed over three ranks. Even when 5-year observation data was used, the evaluated rank of some points varied within the range of uncertainty, thereby highlighting the importance of uncertainty quantification in the wind environment assessment.
机译：本研究扩展了Murakami等人提出的评估框架。 （1986年）通过实施贝叶斯建模，为行人风环境进行全面概率的方法。该方法根据测量的风数据和评估结果来量化构造模型中的不确定性。为了模拟日本最大均值和风向的概率，我们分别使用了威布尔分布和分类分布。定义概率分布的参数是使用贝叶斯技术的概率模型。使用在东京气象观测所测量的风数据，我们证明了该方法的有效性。结果表明，Weibull分布的风向概率和每个参数可以以后概率密度函数的形式估计。使用构造的模型，我们预测了每日最大瞬时风速的概率，并在城市模型中评估了风环境指数（等级）。我们提供了概率分布形式的离散等级，这使我们能够量化评估不确定性。此外，我们阐明了改变用于模型构造的数据量的效果。概率的不确定性随数据量减少。当使用只有1年的数据时，一些评估点可能会发生三个级别。即使使用5年观测数据，也在不确定性范围内变化的某些点的评估等级，从而突出了风环境评估中不确定性量化的重要性。

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