• Wind and Structures
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• v.6 no.5
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• pp.357-386
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• 2003

A number of methods based on various ideas have been proposed for simulating the non-Gaussian stationary process. However, these methods have some limitations. This paper reviewed several simulation methods based on the translation method using logarithmic and polynomial functions, which have emerged in the history of statistics and in the field of civil engineering. The applicability of each method is discussed from the viewpoint of the reproducibility of higher order statistics of the object function in the simulated sample functions, and examined using pressure signals measured from wind tunnel experiments for various shapes of buildings. The parameter estimation methods, i.e. the method of moments and quantile plot, are also reviewed, and the useful aspects of each method are discussed. Additionally, a simple worksheet for parameter estimation is derived based on the method of moment for practical application, and the accuracy is discussed comparing with a set of previously proposed formulae.

• Wind and Structures
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• v.30 no.3
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• pp.299-316
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• 2020

Based on wind tunnel tests, this paper investigates the aerodynamic behavior of a large span canopy roof with elliptical plan and hyperbolic paraboloid shape. The statistics of pressure coefficients and the peak factor distributions are calculated for the top and bottom faces of the roof, and the Gaussian or non-Gaussian characteristics of the pressure time-histories in different areas of the roof are discussed. The cross-correlation of pressures at different positions on the roof, and between the top and bottom faces is also investigated. Combination factors are also evaluated to take into account the extreme values of net loads, relevant to the structural design of canopies.

• Wind and Structures
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• v.31 no.5
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• pp.473-482
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• 2020

The first-order method for estimating the extreme wind pressure on building envelopes with consideration of the directionality of wind speed and wind pressure is improved to enhance its computational efficiency. In this improved method, the result is obtained directly from the empirical distribution of a random selection of annual maximum wind pressure samples generated by a Monte Carlo method, rather than from the previously utilized extreme wind pressure probability distribution. A discussion of the relationship between the first- and full-order methods indicates that when extreme wind pressures in a non-typhoon climate with a high return period are estimated with consideration of directionality, using the relatively simple first-order method instead of the computationally intensive full-order method is reasonable. The validation of this reasonableness is equivalent to validating two assumptions to improve its computational efficiency: 1) The result obtained by the full-order method is conservative when the extreme wind pressure events among different sectors are independent. 2) The result obtained by the first-order method for a high return period is not significantly affected when the extreme wind speeds among the different sectors are assumed to be independent. These two assumptions are validated by examples in different regions and theoretical derivation.

• Wind and Structures
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• v.21 no.1
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• pp.1-23
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• 2015

Wind tunnel testing of a low-rise building with openings (holes) of different sizes and shapes on a roof corner is conducted to measure the internal and external pressures from the building model. Detailed analysis of the testing data is carried out to investigate the characteristics of the internal and external pressures of the building with different openings' configurations. Superimposition of the internal and external pressures makes the emergence of positive net pressures on the roof. The internal pressures demonstrate an overall uniform distribution. The probability density function (PDF) of the internal pressures is close to the Gaussian distribution. Compared with the PDF of the external pressures, the non-Gaussian characteristics of the net pressures weakened. The internal pressures exhibit strong correlation in frequency domain. There appear two humps in the spectra of the internal pressures, which correspond to the Helmholtz frequency and vortex shedding frequency, respectively. But, the peak for the vortex shedding frequency is offset for the net pressures. Furthermore, the internal pressure characteristics indirectly reflect that the length of the front edge enhances the development of the conical vortices.The objective of this study aims to further understanding of the characteristics of internal, external and net pressures for low-rise buildings in an effort to reduce wind damages to residential buildings.

• Wind and Structures
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• v.2 no.2
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• pp.85-94
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• 1999

The EARPG(1)/UPS was first developed by Seong (1993) and has been tested for wind pressure time series simulations (Seong and Peterka 1993, 1997, 1998) to prove its excellent performance for generating non-Gaussian time series, in particular, with large amplitude sharp peaks. This paper presents a parametric study focused on simulation of extreme value statistics based on the synthetic realizations of the EARPG(1)/UPS. The method is shown to have a great capability to simulate a wide range of non-Gaussian statistic values and extreme value statistics with exact target sample power spectrum. The variation of skewed long tail in PDF and extreme value distribution are illustrated as function of relevant parameters.

• Wind and Structures
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• v.25 no.2
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• pp.101-129
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• 2017

Local transient extreme wind loads caused by group tower-related interference are among the major reasons that lead to wind-induced damage of super-large cooling towers. Four-tower arrangements are the most commonly seen patterns for super-large cooling towers. We considered five typical four-tower arrangements in engineering practice, namely, single row, rectangular, rhombic, L-shaped, and oblique L-shaped. Wind tunnel tests for rigid body were performed to determine the influence of different arrangements on static and dynamic wind loads and extreme interference effect. The most unfavorable working conditions (i.e., the largest overall wind loads) were determined based on the overall aerodynamic coefficient under different four-tower arrangements. Then we calculated the one-, two- and three-dimensional aerodynamic loads under different four-tower arrangements. Statistical analyses were performed on the wind pressure signals in the amplitude and time domains under the most unfavorable working conditions. On this basis, the non-Gaussian distribution characteristics of aerodynamic loads on the surface of the cooling towers under different four-tower arrangements were analyzed. We applied the Sadek-Simiu procedure to the calculation of two- and three-dimensional aerodynamic loads in the cooling towers under the four-tower arrangements, and the extreme wind load distribution patterns under the most unfavorable working conditions in each arrangement were compared. Finally, we proposed a uniform equation for fitting the extreme wind loads under the four-tower arrangements; the accuracy and reliability of the equation were verified. Our research findings will contribute to the optimization of the four-tower arrangements and the determination of extreme wind loads of super-large cooling towers.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.