American Society of Civil Engineers


Polynomial Chaos Decomposition for the Simulation of Non-Gaussian Nonstationary Stochastic Processes


by Shigehiro Sakamoto, (Earthquake and Wind Engrg. Section, Building Res. Dept., Taisei Res. Inst., Taisei Corporation, Nase-cho 334-1, Totsukaku Yokohama 245-0051, Japan) and Roger Ghanem, M.ASCE, (Assoc. Prof., Dept. of Civ. Engrg., Johns Hopkins Univ., Baltimore, MD 21218. E-mail: ghanem@jhu.edu)

Journal of Engineering Mechanics, Vol. 128, No. 2, February 2002, pp. 190-201, (doi:  http://dx.doi.org/10.1061/(ASCE)0733-9399(2002)128:2(190))

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Document type: Journal Paper
Abstract: A method is developed for representing and synthesizing random processes that have been specified by their two-point correlation function and their nonstationary marginal probability density functions. The target process is represented as a polynomial transformation of an appropriate Gaussian process. The target correlation structure is decomposed according to the Karhunen-Loève expansion of the underlying Gaussian process. A sequence of polynomial transformations in this process is then used to match the one-point marginal probability density functions. The method results in a representation of a stochastic process that is particularly well suited for implementation with the spectral stochastic finite element method as well as for general purpose simulation of realizations of these processes.


ASCE Subject Headings:
Decomposition
Stochastic processes
Polynomials
Gaussian process
Stationary processes
Simulation