Estimation of Peak Factor of Non-Gaussian Wind Pressures by Improved Moment-Based Hermite Model
Publication: Journal of Engineering Mechanics
Volume 143, Issue 7
Abstract
The moment-based Hermite polynomial function model approach is often used to estimate the extreme value distribution and peak factor of a non-Gaussian process through those of the underlying Gaussian process. This paper presents a study on the performance of the moment-based model approach as applied to various non-Gaussian wind pressures on a large-span saddle-type roof by comparing the estimated peak factors with those directly derived from long-term wind-tunnel data. The results showed that the moment-based model approach can be less accurate for large amounts of non-Gaussian pressure data. One of the reasons is that the skewness and kurtosis are statistical moments affected by both positive and negative probability distribution tails, and thus are less specific in defining only one of the distribution tails, which determines the statistics of maximum or minimum. To improve the accuracy of the moment-based model approach, a new strategy is introduced that defines new statistical moments using the distribution greater or lower than the median for estimation of the distribution of maximum or minimum, respectively. Accordingly, the distributions of maximum and minimum are addressed separately using newly defined two sets of statistical moments with zero skewness. The effectiveness of the newly proposed approach is examined for various non-Gaussian wind pressures.
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Acknowledgments
The support provided in part by NSF Grant No. CMMI-1029922, National Nature Science Foundation of China (91215302, 51478401), and 111 Project of China (B13002) are greatly acknowledged. The authors also thank Dr. Jie Ding for valuable discussion and comments.
References
Choi, M., and Sweetman, B. (2010). “The Hermite moment model for highly skewed response with application to tension leg platforms.” J. Offshore Mech. Arctic Eng., 132(2), .
Davenport, A. G. (1964). “Note on the distribution of the largest value of a random function with application to gust loading.” Proc. Inst. Civ. Eng., 28(2), 187–196.
DeCarlo, L. T. (1997). “On the meaning and use of kurtosis.” Psychol. Methods, 2(3), 292–307.
Deodatis, G., and Micaletti, R. (2001). “Simulation of highly skewed non-Gaussian stochastic processes.” J. Eng. Mech., 1284–1295.
Ding, J., and Chen, X. (2014). “Assessment of methods for extreme value analysis of non-Gaussian wind effects with short-term time history samples.” Eng. Struct., 80, 75–88.
Ding, J., and Chen, X. (2015). “Moment-based translation model for hardening non-Gaussian response processes.” J. Eng. Mech., .
Fitzwater, L. M., and Winterstein, S. R. (2001). “Predicting design wind turbine loads from limited data: Comparing random process and random peak models.” J. Solar Energy Eng., 123(4), 364–371.
Grigoriu, M. (1984). “Crossings of non-Gaussian translation processes.” J. Eng. Mech., 610–620.
Grigoriu, M. (1995). Applied non-Gaussian processes: Examples, theory, simulation, linear random vibration, and MATLAB solutions, PTR Prentice Hall, Upper Saddle River, NJ.
Huang, M. F., Lou, W. J., Chan, C. M., Lin, N., and Pan, X. T. (2013). “Peak distributions and peak factors of wind-induced pressure processes on tall buildings.” J. Eng. Mech., 1744–1756.
Kown, D. K., and Kareem, A. (2011). “Peak factors for non-Gaussian load effects revisited.” J. Struct. Eng., 1611–1619.
Kumar, K. S., and Sathopoulos, T. (2000). “Wind loads on low building roofs: A stochastic perspective.” J. Struct. Eng., 944–956.
Liu, M., Chen, X., and Yang, Q. (2016). “Characteristics of dynamic pressures on a saddle type roof in various boundary layer flows.” J. Wind Eng. Ind. Aerodyn., 150, 1–14.
Masters, F., and Gurley, K. (2003). “Non-Gaussian simulation: Cumulative distribution function map-based spectral correction.” J. Eng. Mech., 1418–1428.
Peng, X., Yang, L., Gavanski, E., Gurley, K., and Prevatt, D. (2014). “A comparison of methods to estimate peak wind loads on buildings.” J. Wind Eng. Ind. Aerodyn., 126, 11–23.
Ragan, P., and Manuel, L. (2008). “Statistical extrapolation methods for estimating wind turbine extreme loads.” J. Solar Energy Eng., 130(3), .
Sadek, F., and Simiu, E. (2002). “Peak non-Gaussian wind effects for database-assisted low-rise building design.” J. Eng. Mech., 530–539.
Tieleman, H. W., Elsayed, M. A. K., Ge, Z., and Hajj, M. R. (2008). “Extreme value distributions for peak pressure and load coefficients. ”J. Wind Eng. Ind. Aerodyn., 96(6–7), 1111–1123.
Winterstein, S. R. (1987). “Moment-based Hermite models of random vibration.”, Dept. of Structural Engineering, Technical Univ. of Denmark, Lyngby, Denmark.
Winterstein, S. R. (1988). “Nonlinear vibration models for extremes and fatigue.” J. Eng. Mech., 1772–1790.
Winterstein, S. R., and Kashef, T. (2000). “Moment-based load and response models with wind engineering applications.” J. Solar Energy Eng., 122(3), 122–128.
Winterstein, S. R., and MacKenzie, C. A. (2013). “Extremes of nonlinear vibration: Models based on moments, L-moments and maximum entropy.” J. Offshore Mech. Arctic Eng., 135(2), .
Yang, L., Gurley, K. R., and Prevatt, D. O. (2013). “Probabilistic modeling of wind pressure on low-rise buildings.” J. Wind Eng. Ind. Aerodyn., 114, 18–26.
Yang, Q., and Tian, Y. (2015). “A model of probability density function of non-Gaussian wind pressure with multiple samples.” J. Wind Eng. Ind. Aerodyn., 140, 67–78.
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Published In
Journal of Engineering Mechanics
Volume 143 • Issue 7 • July 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Oct 21, 2015
Accepted: Nov 14, 2016
Published ahead of print: Feb 23, 2017
Published online: Feb 24, 2017
Published in print: Jul 1, 2017
Discussion open until: Jul 24, 2017
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