Abstract
Wind load is among the control loads for large-span spatial structures. Wind tunnel test is one of the commonly used methods for measuring wind pressure fields of different kinds of structures. However, due to the limited wind pressure data obtained from wind tunnel testing, it is quite meaningful to employ the limited measured data to predict the unknown wind pressure at target points. Considering the complexity of wind pressure fields of large-span spatial structures, a simplified nonparametric method based on conditional simulation is proposed to predict the unknown pressures using the existing data. The Karhunen–Loève (KL for short) expansion is employed to represent wind pressure random variants as eigenfunctions of the covariance operator. To reduce the variant dimensionality, the nearest neighboring estimator is given for the transition distribution of the KL expansion. The targeted wind pressure fields are obtained by expanding the Fourier basis of the eigenfunction and estimating its expansion coefficients. The proposed method is applied to estimate wind pressures on a gable roof building. The relevant parameters of the wind pressure field are obtained, and the results compare well with those from wind tunnel testing, with higher efficiency. The proposed method effectively reduces the dimensionality of the predicted wind pressures, with reduced errors, higher accuracy, and increased efficiency.
Highlights
Wind load is one of the control loads for large-span spatial structures. e current structural design specifications [1] are not specified for the calculation of the wind pressure of this type of structure
There are mainly two kinds of conditional simulation methods [2]: the Kriging method and the conditional probability density function method. e Kriging method is mainly through the random variable to be estimated expressing as a linear combination of several known reference variables and the corresponding correlation function. e conditional probability density function method was first proposed by Kamenda and Morikawa, applied to earthquake engineering [3]. is method mainly uses the joint probability distribution function between variables to estimate unknown variables
Considering the complexity of the random variables of the wind pressure field of large-span spatial structures, this paper proposes a conditional simulation method of wind pressure using the nearest neighbor estimation of the function transition distribution, which is a nonparametric conditional simulation method. is method first uses Karhunen–Loeve to expand the wind pressure random variable as the eigenfunction of the covariance operator, and the nearest neighbor estimate of the KL extended transition distribution is given. en, the expansion coefficient is estimated by extending the Fourier basis of the eigenfunction to realize the estimation of the eigenfunction and obtain the target wind pressure value
Summary
A Conditional Simulation Method for Predicting Wind Pressure Fields of Large-Span Spatial Structures. Wind tunnel test is one of the commonly used methods for measuring wind pressure fields of different kinds of structures. Due to the limited wind pressure data obtained from wind tunnel testing, it is quite meaningful to employ the limited measured data to predict the unknown wind pressure at target points. Considering the complexity of wind pressure fields of large-span spatial structures, a simplified nonparametric method based on conditional simulation is proposed to predict the unknown pressures using the existing data. E targeted wind pressure fields are obtained by expanding the Fourier basis of the eigenfunction and estimating its expansion coefficients. E proposed method effectively reduces the dimensionality of the predicted wind pressures, with reduced errors, higher accuracy, and increased efficiency To reduce the variant dimensionality, the nearest neighboring estimator is given for the transition distribution of the KL expansion. e targeted wind pressure fields are obtained by expanding the Fourier basis of the eigenfunction and estimating its expansion coefficients. e proposed method is applied to estimate wind pressures on a gable roof building. e relevant parameters of the wind pressure field are obtained, and the results compare well with those from wind tunnel testing, with higher efficiency. e proposed method effectively reduces the dimensionality of the predicted wind pressures, with reduced errors, higher accuracy, and increased efficiency
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