Peak Distributions and Peak Factors of Wind-Induced Pressure Processes on Tall Buildings

Huang, Mingfeng

doi:10.1007/978-981-10-1744-5_4

Cited by 6 publications

(11 citation statements)

References 29 publications

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“…In general, Gaussian approximations yield non-conservative estimates of the PF values when applied to non-Gaussian processes (Kareem and Zhao 1994), and different approximate models have been developed to estimate the PF statistics of non-Gaussian processes (Huang et al 2013; Kareem and Zhao 1994; Kwon and Kareem 2011; Sadek and Simiu 2002). Very few studies available in the literature investigated the PFs and their statistics for hyperbolic paraboloid roofs (HPRs) (Liu et al 2016(Liu et al , 2017Rizzo et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Rizzo et al (2018) investigated the non-Gaussianity of the pressure coefficient processes for a square HPR through a comparison between experimental measures and analytical estimates of PF statistics. They observed that: (1) the distribution of non-Gaussian regions strongly depend on the wind angle, (2) the Davenport model systematically underestimates the PF mean and standard deviation in highly non-Gaussian regions, (3) moment-based Hermite models provide accurate estimates of the PF mean, and (4) the Translated-Peak-Process (TPP) model (Huang et al 2013) seems to provide the best estimates of the PF standard deviation. Rizzo et al (2018) also observed that the local variability of the pressure coefficient peaks is best described by a Weibull distribution, as assumed by the TPP model.…”

Section: Introductionmentioning

confidence: 99%

“…are polynomial functions given inYang et al (2013) The analytical estimates of the peak factor's mean and standard deviation for the TTP model(Huang et al 2013) and duration T are given by:…”

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confidence: 99%

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Shape dependence of wind pressure peak factor statistics in hyperbolic paraboloid roofs

Rizzo

Barbato

Sepe

2021

Journal of Building Engineering

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The complex shape dependence of wind pressure's peak factors (PFs) makes it difficult to predict their values for all geometries by using analytical models. Thus, experimental studies are necessary to validate available or new analytical models for each specific shape and geometry. Very little information is available in the literature for the hyperbolic paraboloid geometry, which is frequently used for cable net and membrane roofs that are particularly appropriate for covering large spans. This paper focuses on wind pressure's PFs for this specific geometry. It presents a statistical analysis of experimental data taken from wind tunnel experiments on eight different geometries, obtained through the combinations of squared and rectangular plan shapes, high and low models, and two different curvatures of the roof parabolas. Roof zones for which a non-Gaussian behavior is predominant are identified. A comparison is made between PF statistics obtained experimentally and those estimated by the Davenport, modified Hermite, and Translated Peak Process (TPP) models, in terms of means and standard deviation errors estimated over the entire roofs. It is concluded that the modified Hermite and the TPP models provide the best estimates of the PF means and standard deviations, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shape dependence of wind pressure peak factor statistics in hyperbolic paraboloid roofs

Rizzo

Barbato

Sepe

2021

Journal of Building Engineering

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“…e object of extreme wind pressure estimation can be the parent distribution or the extracted extreme value distribution. When focusing on the parent distribution, the analysis will be carried out with the help of the Hermite polynomial transformation from Gaussian distribution to non-Gaussian distribution [3] and the cumulative probability function [4,5]. e main representatives are the peak factor method [6] proposed by Davenport in 1964 and the follow-up revised methods [7,8].…”

Section: Introductionmentioning

confidence: 99%

Numerical Statistic Approach for Peak Factor of Non‐Gaussian Wind Pressure on Building Claddings

Zhu

Tan

2021

Advances in Civil Engineering

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Wind-induced pressures on high-rise buildings claddings are mostly non-Gaussian distribution, and there is a one-to-one relationship between a specified guarantee rate and its corresponding peak factor. In this study, a stepwise search method for calculating the peak factor of non-Gaussian wind pressure and a gradual independent segmentation method for extracting independent peak values are proposed to determine the relationship accurately. Based on the pressure data of a high-rise building obtained from a rigid model wind tunnel test, the peak factors of non-Gaussian wind pressures on claddings are calculated and compared by using several typical methods. The value of the peak factor and its error rate calculated by several methods is compared with the observed average peak value, and the conversion between the guaranteed rate and the peak factor is discussed. Based on the reliability theory, the true distribution of wind pressure time history was approached infinitely through an efficient numerical method in the process of stepwise search. Compared with the classical Sadek–Simiu method, the proposed stepwise search method achieves improved overall accuracy and applicability. The non-Gaussian features are found to be prominent at leading edge airflow separation on the crosswind side, the leeward corner cuts, the windward corner cuts, and the junction of two leeward surfaces at 45° wind direction angle of square section. The junction of two leeward surfaces at 45° wind direction angle exhibits stronger non-Gaussian features than the crosswind surface at 0° wind direction angle. By giving the identical guarantee rate, the peak factors tend to be much larger in the regions with strong non-Gaussian properties and vice versa.

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“…According to the mentioned issues, the study and analysis of tall structures under the wind pressure influence is necessary to ensure the safety and well-being of cities' residents . For example, pressure and suction caused by wind in various aspects of high-rise structures is an effective parameter in architectural and structural design of high-rise buildings (Huang, 2017;Cao et al, 2009). The pressure and suction resulted from the wind is a kind of random loading that depends on various parameters, such as structural shape, structural dimensions, environmental density, wind potentials and architectural and structural characteristics of the building (Lin et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Effect of Wind-Structure Interaction on Longitudinal Response of Tall Buildings

Niknamian¹

2019

Preprint

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Since rapid population growth leads to tall-buildings construction, several studies are conducted regarding effective parameters on tall-structures behavior against wind pressure, which indicate wind speed changes, structures’ damping and environmental context density are effective on structure’s aerodynamic and aerostatic behavior. Accordingly, using CFD, CSD and CARRC, we investigated impact of mentioned factors on structure-wind interaction. Here, several 3D standard models were generated using ABAQUS software, next, wind profile was modeled in atmospheric boundary layer, flow turbulence was simulated by LES method and simulation method was used to transfer non-uniform loads from fluid to structural nodes. Structural damping was also determined by Rayleigh-method. Results show that structures without damping have much higher responses to wind. Furthermore, denser contexts around the structure change wind velocity profile at height and increase maximum displacements and stresses in upper and lower parts; average wind pressure distribution in high-rise buildings is influenced by average wind speed.

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Peak Distributions and Peak Factors of Wind-Induced Pressure Processes on Tall Buildings

Cited by 6 publications

References 29 publications

Shape dependence of wind pressure peak factor statistics in hyperbolic paraboloid roofs

Shape dependence of wind pressure peak factor statistics in hyperbolic paraboloid roofs

Numerical Statistic Approach for Peak Factor of Non‐Gaussian Wind Pressure on Building Claddings

Effect of Wind-Structure Interaction on Longitudinal Response of Tall Buildings

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