Statistical evaluation of wind properties based on long-term monitoring data
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ORIGINAL PAPER
Statistical evaluation of wind properties based on long‑term monitoring data Xiao‑Wei Ye1 · Yang Ding1 · Hua‑Ping Wan1 Received: 21 May 2020 / Revised: 3 July 2020 / Accepted: 4 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Wind speed and wind direction are two important factors to describe the wind properties. The statistical probability model of wind speed and wind direction is widely used to characterize the uncertainty of wind field around the structures. Considering the correlation between the wind speed and wind direction, they should be modeled simultaneously, which highlights the importance of using joint probability density function (JPDF) to describe the wind properties. An angular–linear (AL) model is employed to construct the JPDF of wind speed and wind direction based on long-term monitoring data. The finite mixture (FM) Gumbel distribution, which models the wind speed, and the FM von Mises distribution, which models the wind direction, are proposed to formulate the AL model. Expectation–maximization (EM) and genetic algorithm (GA) are adopted to estimate the AL model parameters. For the EM method, the analytical expressions for calculating the AL model parameters are derived. For the GA method, the analytical fitness functions are derived. These derived analytical expressions facilitate the implementation of the EM and GA methods. One-year wind monitoring data collected by structural health monitoring (SHM) system installed on Jiubao Bridge is employed to demonstrate the feasibility of the AL model-based method in evaluating the wind properties. The results show that the AL model formulated by the FM Gumbel distribution and FM Von Mises distribution is effective for establishing JPDF for joint modeling of the wind speed and wind direction. In the AL model, the EM method is more effective for estimating the parameters of the FM von Mises distribution, whereas the GA methods is more powerful for estimating the parameters of the FM Gumbel distribution. Keywords Structural health monitoring · Wind properties · Angular–linear model · Finite mixture distribution · Expectation–maximization · Genetic algorithm · Joint probability density function
1 Introduction Wind effects (especially typhoons or hurricanes, thunderstorm downbursts, and tornados) have drawn considerable attentions from the meteorological and wind engineering communities since wind can cause destructive damage to infrastructure systems as well as significant human and economic losses [1, 2]. Due to the advance of design and construction technologies and emergence of novel materials, the structures are becoming taller, lighter, and longer (e.g., high-rise buildings and long-span bridges). The characteristics of light mass, high flexibility, and slight damping make them become more wind sensitive and more vulnerable to * Hua‑Ping Wan [email protected] 1
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
excessive wind excitation. For example, many long-span bri
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