Error Assessment for the Coherency Matrix-Based Spectral Representation Method in Multivariate Random Processes Simulation
Publication: Journal of Engineering Mechanics
Volume 139, Issue 9
Abstract
Multivariate random processes are usually simulated by the spectral representation method (SRM). According to the matrix for decomposition, the SRM has two main types, that is, the SRM based on the decomposition of the power spectral density (PSD) matrix denoting the PSD matrix-based SRM, and the SRM based on the decomposition of the coherency matrix denoting the coherency matrix based-SRM. The stochastic errors of the PSD for the PSD matrix-based SRM have been given. This paper presents the stochastic errors of the PSD for the coherency matrix-based SRM, and makes a comparison of these errors for the PSD matrix-based SRM. For the random amplitudes formulas and random phase formula and Cholesky decomposition method, the stochastic errors of the PSDs for the PSD matrix-based SRM are the same as or the coherency matrix-based SRM, whereas for the random phases formula and eigendecomposition method and random phases formula and root decomposition method, they are different. However, the differences are slight when taking into account the sum of the PSD functions’ stochastic errors.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research is supported by the National Natural Science Foundation of China (Grant Nos. 51278382 and 90815020).
References
Bendat, J. S., and Piersol, A. G. (1986). Random data: Analysis and measurement procedures, Wiley, New York.
Bocchini, P., and Deodatis, G. (2008). “Critical review and latest developments of a class of simulation algorithms for strongly non-Gaussian random fields.” Probab. Eng. Mech., 23(4), 393–407.
Deodatis, G. (1996a). “Non-stationary stochastic vector processes: seismic ground motion applications.” Probab. Eng. Mech., 11(3), 149–168.
Deodatis, G. (1996b). “Simulation of ergodic multivariate stochastic processes.” J. Eng. Mech., 122(8), 778–787.
Deodatis, G., and Micaletti, R. C. (2001). “Simulation of highly skewed non-Gaussian stochastic processes.” J. Eng. Mech., 127(12), 1284–1295.
Ferrante, F. J., Arwade, S. R., and Graham-Brady, L. L. (2005). “A translation model for non-stationary, non-Gaussian random processes.” Probab. Eng. Mech., 20(3), 215–228.
Gao, Y., et al. (2012a). “Error assessment for spectral representation method in random field simulation.” J. Eng. Mech., 138(6), 711–715.
Gao, Y., Wu, Y., Li, D., Liu, H., and Zhang, N. (2012b). “An improved approximation for the spectral representation method in the simulation of spatially varying ground motions.” Probab. Eng. Mech., 29(1), 7–15.
Grigoriu, M. (1998). “Simulation of stationary non-Gaussian translation processes.” J. Eng. Mech., 124(2), 121–126.
Grigoriu, M. (2004). “Spectral representation for a class of non-Gaussian processes.” J. Eng. Mech., 130(5), 541–546.
Grigoriu, M. (2008). “A class of weakly stationary non-Gaussian models.” Probab. Eng. Mech., 23(4), 378–384.
Grigoriu, M. (2011). “Linear models for non-Gaussian processes and applications to linear random vibration.” Probab. Eng. Mech., 26(3), 461–470.
Grigoriu, M., Ditlevsen, O., and Arwade, S. R. (2003). “A Monte Carlo simulation model for stationary non-Gaussian processes.” Probab. Eng. Mech., 18(1), 87–95.
Hanrichandran, R. S., and Vanmarcke, E. H. (1986). “Stochastic variation of earthquake ground motion in space and time.” J. Eng. Mech., 112(2), 154–174.
Hu, L., Li, L., Fan, J., and Fang, Q. (2006). “Coherency matrix-based proper orthogonal decomposition with application to wind field simulation.” Earthq. Eng. Eng. Vib., 5(2), 267–272.
Hu, L., Li, L., and Gu, M. (2010). “Error assessment for spectral representation method in wind velocity field simulation.” J. Eng. Mech., 136(9), 1090–1104.
Popescu, R., Deodatis, G., and Prevost, J. H. (1998). “Simulation of homogeneous nonGaussian stochastic vector fields.” Probab. Eng. Mech., 13(1), 1–13.
Rice, S. O. (1954). “Mathematical analysis of random noise.” Selected papers on noise and stochastic processes, N. Wax, ed., Dover, New York, 133–294.
Sakamoto, S., and Ghanem, R. (2002). “Polynomial chaos decomposition for the simulation of non-Gaussian nonstationary stochastic processes.” J. Eng. Mech., 128(2), 190–201.
Shi, Y. (2006). “Simulation of stationary non-Gaussian stochastic processes/fields with application in suspension bridge cable strength estimation.” Ph.D. thesis, Columbia Univ., New York.
Shinozuka, M., and Deodatis, G. (1991). “Simulation of stochastic processes by spectral representation.” Appl. Mech. Rev., 44(4), 191–204.
Shinozuka, M., and Deodatis, G. (1996). “Simulation of multi-dimensional Gaussian stochastic fields by spectral representation.” Appl. Mech. Rev., 49(1), 29–53.
Shinozuka, M., and Jan, C. M. (1972). “Digital simulation of random processes and its application.” J. Sound Vibrat., 36(1–3), 829–843.
Wu, Y., Gao, Y., and Li, D. (2011). “Simulation of spatially correlated earthquake ground motions for engineering purposes.” Earthq. Eng. Eng. Vib., 10(2), 163–173.
Yang, J. N. (1972). “Simulation of random envelope processes.” J. Sound Vibrat., 21(1), 73–85.
Information & Authors
Information
Published In
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jan 11, 2012
Accepted: Nov 2, 2012
Published online: Nov 5, 2012
Published in print: Sep 1, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.