Simulation of Highest-Order Extreme Values of Correlated Random Process
Publication: Journal of Engineering Mechanics
Volume 142, Issue 1
Abstract
Several existing methods on the extreme value estimation and signal simulation are reviewed in this study with evaluation of their performances in the simulation of the highest-order extreme values. The limitations of these methods are also discussed. Then, a new method to simulate the highest-order extreme values of a random process is presented with improvement to these limitations. Extreme value theory is involved in the estimation and Monte Carlo simulation is used in the numerical study. Estimation of the annual top 10 highest 10-min mean wind speeds is conducted for illustration. Results show that the top-order extreme values of the correlated random processes can be well simulated by the proposed method.
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Acknowledgments
This study was funded by National Natural Science Foundation of China (51408207, 91215302). It is also supported by the 111 Project (B13002). The authors give special thanks to Prof. S.S. Law, Prof. X. Chen, and Prof. H. Kawai for their valuable suggestions and comments.
References
Cook, N. J. (1982). “Towards better estimation of extreme winds.” J. Wind Eng. Ind. Aerodyn., 9(3), 295–323.
Cook, N. J., and Mayne, J. R. (1980). “A refined working approach to the assessment of wind loads for equivalent static design.” J. Wind Eng. Ind. Aerodyn., 6(1–2), 125–137.
Filho, J. C. S. S., and Yacoub, M. D. (2008). “Coloring non-Gaussian sequences.” IEEE Trans. Signal Process., 56(12), 5817–5822.
Grigoriu, M. (2000). “Non-Gaussian models for stochastic mechanics.” Probab. Eng. Mech., 15(1), 15–23.
Grigoriu, M. (2007). “Parameter translation models for stationary non-Gaussian processes and fields.” J. Sound Vib., 303(3–5), 428–439.
Gumbel, E. J. (1958). Statistics of extremes, Columbia University Press, New York.
Gumley, S. J., and Wood, C. J. (1982). “A discussion of extreme wind-loading probabilities.” J. Wind Eng. Ind. Aerodyn., 10(1), 31–45.
Harris, R. I. (2009). “XIMIS, a penultimate extreme value method suitable for all types of wind climate.” J. Wind Eng. Ind. Aerodyn., 97(5–6), 271–286.
Hui, Y., Yang, Q. S., and Li, Z. N. (2014). “An alternative method for estimation of annual extreme wind speed.” Wind Struct., 19(2), 169–184.
Kaderli, A., and Kayhan, A. S. (2000). “Spectral estimation of ARMA processes using ARMA-cepstrum recursion.” IEEE Signal Process. Lett., 7(9), 259–261.
Kizilkaya, A., and Kayran, A. H. (2006). “ARMA model parameter estimation based on the equivalent MA approach.” Digital Signal Process., 16(6), 670–681.
Kotz, S., and Nadarajah, S. (2000). Extreme value distributions: Theory and applications, Imperial College Press, London, U.K.
Li, Y., and Kareem, A. (1990). “ARMA representation of wind field.” J. Wind Eng. Ind. Aerodyn., 36(1), 415–427.
Liu, B., and Munson, D. C. (1982). “Generation of a random sequence having a jointly specified marginal distribution and autocovariance.” IEEE Trans. Acoust. Speech, Signal Process., 30(6), 973–983.
Masters, F., and Gurley, K. R. (2003). “Non-Gaussian simulation: Cumulative distribution function map-based spectral correction.” J. Eng. Mech., 1418–1428.
Shinozuka, M. (1971). “Simulation of multivariate and multidimensional random processes.” J. Acoust. Soc. Am., 49(1B), 357–368.
Simiu, E., and Heckert, N. A. (1996). “Extreme wind distribution tails: A peaks over threshold approach.” J. Struct. Eng., 539–547.
Yamazaki, F., and Shinozuka, M. (1988). “Digital generation of non-Gaussian stochastic fields.” J. Eng. Mech., 1183–1197.
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Information
Published In
Journal of Engineering Mechanics
Volume 142 • Issue 1 • January 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Sep 4, 2014
Accepted: Apr 7, 2015
Published online: May 26, 2015
Discussion open until: Oct 26, 2015
Published in print: Jan 1, 2016
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