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Peak Distributions and Peak Factors of Wind-Induced Pressure Processes on Tall Buildings

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Abstract

The emergence of performance-based wind engineering calls for improved probabilistic modeling of wind effects on buildings. This chapter focuses on the development of probabilistic models of the peak distribution and peak factor for non-Gaussian processes and explores the applications of this development in wind engineering. The closed-form expressions for the mean, SD, and fractile levels of extremes are derived for a random process whose peaks are modeled by the parametric Weibull distribution. A new translated-peak-process method is then developed for the estimation of the peak distribution, peak factor, and variability of extremes, based on the Weibull distribution and point-to-point mapping procedure. The proposed translated-peak-process method is validated by wind-tunnel pressure measurements on a standard tall building and is shown to be more robust and practical than many existing methods in analyzing non-Gaussian wind pressure data.

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References

  • Australian/New Zealand Standard. (2002). Structural design actions: Wind actions. AS1170.2:2002. Standards Australia: Sydney.

    Google Scholar 

  • Binh, L. V., Ishihara, T., Phuc, P. V., & Fujino, Y. (2008). A peak factor for non-Gaussian response analysis of wind turbine tower. Journal of Wind Engineering and Industrial Aerodynamics, 96, 2217–2227.

    Article  Google Scholar 

  • Carassale, L., & Brunenghi, M. M. (2011). Statistical analysis of wind-induced pressure fields: A methodological perspective. Journal of Wind Engineering and Industrial Aerodynamics, 99, 700–710.

    Article  Google Scholar 

  • Chen, X., & Huang, G. (2009). Evaluation of peak resultant response for wind-excited tall buildings. Engineering Structures, 31, 858–868.

    Article  Google Scholar 

  • Cheng, P. W., van Bussel, G. J. W., van Kuik, G. A. M., & Vugts, J. H. (2003). Reliability-based design methods to determine the extreme response distribution of offshore wind turbines. Wind Energy, 6, 1–22.

    Article  Google Scholar 

  • Ciampoli, M., & Petrini, F. (2012). Performance-based Aeolian risk assessment and reduction for tall buildings. Probabilistic Engineering Mechanics, 28, 75–84.

    Article  Google Scholar 

  • Davenport, A. G. (1964). Note on the distribution of the largest value of a random function with application to gust loading. Proceedings of the International Civil Engineering, 28, 187–196.

    Google Scholar 

  • Ding, J., & Chen, X. Z. (2015). Moment-Based translation model for hardening non-Gaussian response processes. Journal of Engineering Mechanics, ASCE, 06015006.

    Google Scholar 

  • Ding, J., Gong, K. M., & Chen, X. Z. (2013). Comparison of statistical extrapolation methods for the evaluation of long-term extreme response of wind turbine. Engineering Structures, 57, 100–115.

    Article  Google Scholar 

  • Gioffre, M., Gusella, V., & Grigoriu, M. (2001). Non-Gaussian wind pressure on prismatic buildings. I: stochastic field. Journal of Structural Engineering, ASCE, 127(9), 981–989.

    Google Scholar 

  • Grigoriu M. (1984). Crossing of non-Gaussian translation process. Journal of Engineering Mechanics, ASCE, 110(4), 610–620.

    Google Scholar 

  • Gumbel, E. J. (1958). Statistical of extremes. New York: Columbia Univ. Press.

    MATH  Google Scholar 

  • Gurley, K. R., Tognarelli, M. A., & Kareem, A. (1997). Analysis and simulation tools for wind engineering. Probabilistic Engineering Mechanics, 12(1), 9–31.

    Article  Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning (2nd ed.). New York: Springer.

    Book  MATH  Google Scholar 

  • Huang, M. F., Chan, C. M., Lou, W. J., & Kwok K. C. S. (2012a). Statistical extremes and peak factors in wind-induced vibration of tall buildings. Journal of Zhejiang University Science B (Applied Physics & Engineering), 13(1),18–32.

    Google Scholar 

  • Huang, M. F., Lou, W. J., Chan, C. M., & Bao, S. (2012b). Peak factors of non-Gaussian wind forces on a complex-shaped tall building. The Structural Design of Tall and Special Buildings,. doi:10.1002/tal.763.

    Google Scholar 

  • Huang, M. F., Lou, W., Pan, X., Chan, C. M., & Li, Q. S. (2014). Hermite extreme value estimation of non-Gaussian wind load process on a long-span roof structure. Journal of Structural Engineering, ASCE, 140(9), 04014061.

    Article  Google Scholar 

  • Ishikawa, T. (2004). A study on wind load estimation method considering dynamic effect for overhead transmission lines. Doctoral Thesis, Waseda University, Japan (in Japanese).

    Google Scholar 

  • Kareem, A., & Zhao, J. (1994). Analysis of non-Gaussian surge response of tension leg platforms under wind loads. Journal of Offshore Mechanics and Arctic Engineering, ASME, 116, 137–144.

    Google Scholar 

  • Karpa, O., & Naess, A. (2013). Extreme value statistics of wind speed data by the ACER method. Journal of Wind Engineering and Industrial Aerodynamics, 112, 1–10.

    Article  Google Scholar 

  • Kwon, D., & Kareem, A. (2011). Peak factors for non-Gaussian load effects revisited. Journal of Structural Engineering, ASCE, 137(12), 1611–1619.

    Google Scholar 

  • Lou, W., Zhang, L., Huang, M. F., & Li, S. Q. (2015). Multiobjective equivalent static wind loads on complex tall buildings using non-Gaussian peak factors. Journal of Structural Engineering, ASCE, 141(11), 04015033.

    Article  Google Scholar 

  • Lutes, L. D. (2008). Joint distribution of peaks and valleys in a stochastic process. Probabilistic Engineering Mechanics, 23, 254–266.

    Article  Google Scholar 

  • Michaelov, G., Sarkani, S., & Lutes, L. D. (1996). Fractile levels for non-stationary extreme response of linear structures. Journal of Structural Safety, 18(1), 11–31.

    Article  Google Scholar 

  • Middleton, D. (1960). An introduction to statistical communication theory. New York: McGraw-Hill.

    MATH  Google Scholar 

  • Newland, D. E. (1984). Random vibration and spectral analysis. UK: Longman Scientific & Technical.

    MATH  Google Scholar 

  • Ochi, M. K. (1998). Probability distribution of peaks and troughs of non-Gaussian random process. Probabilistic Engineering Mechanics, 13(4), 291–298.

    Article  Google Scholar 

  • Pillai, S. N., & Tamura, Y. (2009). Generalized peak factor and its application to stationary random processes in wind engineering applications. Journal of Wind Engineering, 6(1), 1–10.

    Google Scholar 

  • Rice, S. O. (1954). Mathematical analysis of random noise. In N. Wax (Ed.), Chapter 4: Select papers on noise and stochastic processes. New York: Dover.

    Google Scholar 

  • Sadek, F. and Simiu, E. (2002). Peak non-Gaussian wind effects for database-assisted low-rise building design. Journal of Engineering Mechanics, ASCE, 128(5), 530–539.

    Google Scholar 

  • Socquet-Juglard, H., Dysthe, K., Trulsen, K., Krogstad, H., & Liu, J. (2005). Probability distributions of surface gravity waves during spectral changes. Journal of Fluid Mechanics, 542, 195–216.

    Article  MathSciNet  MATH  Google Scholar 

  • Tayfun, M. A. (1994). Distributions of envelope and phase in weakly nonlinear random waves. Journal of Engineering Mechanics, ASCE, 120(4), 1009–1025.

    Google Scholar 

  • Tieleman, H. W., Ge, Z., & Hajj, M. R. (2007). Theoretically estimated peak wind loads. Journal of Wind Engineering and Industrial Aerodynamics, 95, 113–132.

    Article  Google Scholar 

  • Tieleman, H. W., Elsayed, M. A. K., Ge, Z., & Hajj, M. R. (2008). Extreme value distributions for peak pressure and load coefficients. Journal of Wind Engineering and Industrial Aerodynamics, 96, 1111–1123.

    Article  Google Scholar 

  • Tognarelli, M. A., Zhao, J., & Kareem, A. (1997). Equivalent statistical cubicization for system and forcing nonlinearities. Journal of Engineering Mechanics, 123(8), 890–893.

    Article  Google Scholar 

  • Winterstein, S. R. (1988). Nonlinear vibration models for extremes and fatigue. Journal Engineering Mechanics, ASCE, 114(10), 1772–1790.

    Google Scholar 

  • Winterstein, S. R., & Kashef, T. (2000). Moment-based load and response models with wind engineering applications. Journal of Solar Energy Engineering, 122(3), 122–128.

    Google Scholar 

  • Winterstein, S. R., Ude, T. C., & Kleiven, G. (1994). Springing and slow-drift responses: Predicted extremes and fatigue vs. simulation. Proceedings, BOSS-94, (Vol. 3, pp. 1–15). Cambridge, MA: Massachusetts Institute of Technology.

    Google Scholar 

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, Zhejiang, China

    Mingfeng Huang

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  1. Mingfeng Huang
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Correspondence to Mingfeng Huang .

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Huang, M. (2017). Peak Distributions and Peak Factors of Wind-Induced Pressure Processes on Tall Buildings. In: High-Rise Buildings under Multi-Hazard Environment. Springer, Singapore. https://doi.org/10.1007/978-981-10-1744-5_4

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  • DOI: https://doi.org/10.1007/978-981-10-1744-5_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1743-8

  • Online ISBN: 978-981-10-1744-5

  • eBook Packages: EngineeringEngineering (R0)

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