Hybrid C- and L-Moment–Based Hermite Transformation Models for Non-Gaussian Processes
Publication: Journal of Engineering Mechanics
Volume 144, Issue 2
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.
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Acknowledgments
This research work is sponsored by China National Science Foundation (Grant No. 51379035), China Key Research Scheme (Grant No. 2016YFC0303706), and Offshore Engineering Development Projects [Grant Nos. (2015)-75, SZHY2014-B01-001, and 201411201645511650] of Shenzhen government.
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Published In
Journal of Engineering Mechanics
Volume 144 • Issue 2 • February 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Jan 11, 2017
Accepted: Aug 4, 2017
Published online: Dec 12, 2017
Published in print: Feb 1, 2018
Discussion open until: May 12, 2018
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