Non-Gaussian properties and their effects on extreme values of wind pressure on the roof of long-span structures

Song, Junhua; Xu, Wei; Hu, Gang; Liang, Shan; Tan, Jian Feng

doi:10.1016/j.jweia.2018.11.027

Cited by 35 publications

(11 citation statements)

References 31 publications

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“…It is highly recommended to consider the extreme value reconstruction on long‐span structures, especially for the identified sensitive regions. The extreme values of fluctuating wind speed is defined in Equation 12 20

x_{extreme} = μ \pm g_{peak} σ^{exa},

in which μ is the mean value of the fluctuating wind speed.…”

Section: Methodsmentioning

confidence: 99%

“…In fact, researchers and engineers are concerned more about the extreme value of wind load, which is used to check the safety of local structures. Accurate estimation of extreme wind load on the roof surface over a certain duration is an essential prerequisite for the performance‐based design of long‐span structures such as airport satellite concourses and stadiums 20 …”

Section: Introductionmentioning

confidence: 99%

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Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

Lin

2021

Structural Contr & Hlth

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Summary Data reconstruction is usually used for lost data recovery and data supplement in structural health monitoring (SHM) system. In this paper, a data reconstruction algorithm is proposed for the entire time series data missing at dispersed and consecutive multiple observation points. Kriging‐based sequence interpolation (KSI) is adopted for primary data reconstruction and probability density function (PDF) reconstruction. In terms of dispersed data missing, the reconstructed PDF is of great accuracy, which is used to calculate the exact standard deviation to correct the primary reconstructed data and obtain satisfactory performance. Regarding the consecutive data missing, the first correction is insufficient due to the missing of too much information. In addition, it is found to exist a fuzzy mapping relation between the exact standard deviation and extreme values of wind speed series. Thus, the quantile regression combined with deep neural network is proposed for probability reconstruction of the extreme values. The point estimations are adopted to the secondary correction for the reconstructed data with first correction. The effectiveness and validity of the secondary correction strategy have been testified by structural dynamic response analysis. The extreme value of the displacement response under the reconstructed data with the secondary correction is closer to that under the actual wind data, compared with the reconstructed data with first correction.

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x_{extreme} = μ \pm g_{peak} σ^{exa},

in which μ is the mean value of the fluctuating wind speed.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

Lin

2021

Structural Contr & Hlth

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“…Meanwhile, [40] discussed the mean up-crossing rates for Gaussian and non-Gaussian processes, laying the groundwork for future analytical models that aim to predict extremes of non-Gaussian processes, as for example the analytical models given by [41,42] and the chaos polynomial theory of which some applications are discussed by [43,44]. This approach was largely used by researcher through application, as for example by [45,46] or to develop new models. Winterstein proposed a nonlinear vibration approach using the Moment-based Hermite model to predict extremes of a random vibration, subsequently applied in [47] on a statistical analysis of tension-leg platforms.…”

Section: From Holmes and Moriarty1999 To Torrielli Et Al 2016 Through Modification Of The Cook And Mayne 1979 Modelmentioning

confidence: 99%

Random Process Peaks Prediction: A Literature Overview

Giovanni¹,

D’Asdia²

2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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The prediction of the maximum or minimum peaks of a random process in the fields of civil and mechanical engineering is a hot topic for the scientific literature. It is known that the maximum of a random process depends on the process length. For this reason, a probabilistic approach is necessary to estimate a reliable value from the process. Researchers for specific case of studies proposed several analytical models to predict maxima of a random process. Mostly, they are grouped on two families, models for Gaussian processes and models for non-Gaussian processes. Each model was computed and calibrated based on a specific experimental campaign for a specific case of study. This makes difficult to select the best model for every application. In addition, codes and standards neglect this topic and one might happen to make the error to assume the maximum value as the statistical maximum of the random process. Commonly, in the field of the structural engineering, peaks of a wind induced, or wind-induced flow acceleration time histories are estimated following the probabilistic approach of Cook and Mayne. However, from 1950 to today hundreds different probabilistic approach was given by literature. The purpose of this paper is to discuss an overview of models grouped by theoretical underlying assumptions.

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“…The Hermite-Davenport Method of Yang et al (2013) From the extensive review of Cp peak estimation methods in section Previous Peak Estimation Methods, it is clear the non-Gaussian peak factor obtained by moment-based Hermite polynomial translation model has received the attention of many researchers. Among several studies dealing with this model, the method suggested by Yang et al (2013), which will be denoted as "YGP method" hereafter, will be selected for our calibration of XIMIS method because of its relatively simple use and accuracy examined by several researchers (Peng et al, 2014;Liu et al, 2017;Ma and Xu, 2017;Song et al, 2019).…”

Section: "Industry Standard" Extreme-value Analysismentioning

confidence: 99%

Evaluation of XIMIS for Assessing Extreme Pressure Coefficients

Gavanski

Cook²

2019

Front. Built Environ.

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Commonly used methods to estimate peak wind pressures on buildings are summarized. The Harris (2009) penultimate XIMIS method is described and calibrated against Gumbel epochal extreme-value analysis (EVA), as well as with the Hermite-Davenport peak factor method by Yang et al. (2013) (YGP) using a very long record of wind tunnel data from many pressure taps. The "industry standard" EVA, comprising sixteen 10-min epochs, gives the best accuracy, but is inefficient in its use of data. YGP is the least accurate, with the largest anomalies underestimating in reattachment zones. XIMIS is comparable to EVA for the same record lengths and remains better than YGP for records up to six times shorter.

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Non-Gaussian properties and their effects on extreme values of wind pressure on the roof of long-span structures

Cited by 35 publications

References 31 publications

Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

Random Process Peaks Prediction: A Literature Overview

Evaluation of XIMIS for Assessing Extreme Pressure Coefficients

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