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Indexed by:期刊论文

Date of Publication:2018-02-01

Journal:JOURNAL OF ENGINEERING MECHANICS

Included Journals:SCIE

Volume:144

Issue:2

ISSN No.:0733-9399

Key Words:Non-Gaussian; Hermite model; Central moment (C-moment); Linear moment (L-moment); Hybrid C/L model

Abstract:The moment-based Hermite transformation models are widely used in extreme-value prediction and fatigue estimation of non-Gaussian processes. However, when only higher-order ordinary central moments (C-moments) are involved in the transformation, the Hermite model would lead to statistical uncertainty. Furthermore, the application of moment-based Hermite models to measured time series is restricted if accurate moments cannot be retrieved from data. In this paper, the respective virtues of C-moments and linear moments (L-moments) are exploited to formulate a new style of nonlinear transformation. Combinations of these two types of moments are sought with various strategies in terms of the accuracy in extreme-value prediction of non-Gaussian processes. It is found that for a process of very strong non-Gaussianity, the quartic C-moment model renders best accuracy when the sampling data are rich, while two of hybrid C- and L-moment (C/L) models work most nicely when data size is limited. (C) 2017 American Society of Civil Engineers.