A model of probability density function of non-Gaussian wind pressure with multiple samples
TL;DR: In this article, an innovative and analytical PDF formula of extreme wind pressure coefficients for multiple samples, which is expressed as a function of the JPDF of mean, standard deviation and the PDF of non-Gaussian peak factor, is presented.
About: This article is published in Journal of Wind Engineering and Industrial Aerodynamics.The article was published on 2015-05-01. It has received 35 citations till now. The article focuses on the topics: Gaussian random field & Gaussian function.
Citations
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TL;DR: The moment-based Hermite polynomial function model approach is often used to estimate the extreme value distribution and peak factor of a non-Gaussian process through those of the underlyin... as discussed by the authors.
Abstract: The moment-based Hermite polynomial function model approach is often used to estimate the extreme value distribution and peak factor of a non-Gaussian process through those of the underlyin...
36 citations
TL;DR: In this article, the authors investigated the characteristics of dynamic pressures on a saddle type roof under normal and corner attacking angles in various boundary layer turbulent flows and simulated six turbulent flows in wind tunnel.
35 citations
TL;DR: In this article, the authors used field measurement data from the Xihoumen Bridge health monitoring system and examined the wind characteristics over water terrain at single point from four typhoons when their outer regions passed the bridge.
27 citations
TL;DR: This work has shown that non-Gaussian processes beset many aspects of structural engineering analysis and various third-order Hermite polynomial models have been proposed and widely used.
Abstract: Non-Gaussian processes beset many aspects of structural engineering analysis. To estimate non-Gaussian processes, various third-order Hermite polynomial models have been proposed and widely...
24 citations
TL;DR: In this article, the peak value of wind pressure is determined using very long time histories of the wind pressure data to evaluate the performance of moment-based HPM and Davenport's formula.
24 citations
References
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TL;DR: The purpose of this book is to bring together existing and new methodologies of random field theory and indicate how they can be applied to these diverse areas where a "deterministic treatment is inefficient and conventional statistics insufficient."
1,639 citations
Book•
01 Jan 1983
TL;DR: In this paper, the authors review the classical theory of multidimensional random processes and introduce basic probability concepts and methods in the random field context and give a concise amount of second-order analysis of homogeneous random fields in both the space-time domain and the wave number-frequency domain.
Abstract: Random variation over space and time is one of the few attributes that might safely be predicted as characterizing almost any given complex system. Random fields or "distributed disorder systems" confront astronomers, physicists, geologists, meteorologists, biologists, and other natural scientists. They appear in the artifacts developed by electrical, mechanical, civil, and other engineers. They even underlie the processes of social and economic change. The purpose of this book is to bring together existing and new methodologies of random field theory and indicate how they can be applied to these diverse areas where a "deterministic treatment is inefficient and conventional statistics insufficient." Many new results and methods are included.After outlining the extent and characteristics of the random field approach, the book reviews the classical theory of multidimensional random processes and introduces basic probability concepts and methods in the random field context. It next gives a concise amount of the second-order analysis of homogeneous random fields, in both the space-time domain and the wave number-frequency domain. This is followed by a chapter on spectral moments and related measures of disorder and on level excursions and extremes of Gaussian and related random fields.After developing a new framework of analysis based on local averages of one-, two-, and n-dimensional processes, the book concludes with a chapter discussing ramifications in the important areas of estimation, prediction, and control. The mathematical prerequisite has been held to basic college-level calculus.
1,518 citations
01 Jun 1964
683 citations
TL;DR: In this paper, Hermite moment models of nonlinear random vibration are formulated, which use response moments (skewness, kurtosis, etc.) to form non-Gaussian contributions, made orthogonal through a Hermite series.
Abstract: Hermite moment models of nonlinear random vibration are formulated. These models use response moments (skewness, kurtosis, etc.) to form non‐Gaussian contributions, made orthogonal through a Hermite series. First‐yield and fatigue failure rates are predicted from these moments, which are often simpler to estimate (from either a time. history or analytical model). Both hardening and softening nonlinear models are developed. These are shown to be more flexible than the conventional Charlier and Edgeworth series, with the ability to reflect wider ranges of nonlinear behavior. Analytical moment‐based estimates of spectral densities, crossing rates, probability distributions of the response and its extremes, and fatigue damage rates are formed. These are found to compare well with exact results for various nonlinear models, including nonlinear oscillator responses and quasi‐static responses to Morison wave loads.
458 citations
Book•
26 Mar 2004
TL;DR: This chapter discusses Random Variables, Linear Models and Linear Regression, and Statistical Inference, Parameter Estimation, and Model Verification, as well as some important Discrete Distributions.
Abstract: Preface1 IntroductionPart A: Probability and Random Variables2 Basic Probability Concepts3 Random Variables and Probability Distributions4 Expectations And Moments5 Functions of Random Variables6 Some Important Discrete Distributions7 Some Important Continuous DistributionsPart B: Statistical Inference, Parameter Estimation, and Model Verification8 Observed Data and Graphical Representation9 Parameter Estimation10 Model Verification11 Linear Models and Linear RegressionAppendix A: TablesAppendix B: Computer SoftwareAppendix C: Answers to Selected ProblemsSubject Index
370 citations