Skip to main content
SpringerLink
Log in

Extrapolation reconstruction of wind pressure fields on the claddings of high-rise buildings

  • Research Article
  • Published:
Frontiers of Structural and Civil Engineering Aims and scope Submit manuscript

Abstract

Recent research about reconstruction methods mainly used the interpolation reconstruction of the fluctuating wind pressure field on the surface. However, to investigate wind pressure at the edge of the building, the work presented in this paper focuses on the extrapolation reconstruction of wind pressure fields. Here, we propose an improved proper orthogonal decomposition (POD) and Kriging method with a von Kármán correlation function to resolve this issue. The studies show that it works well for not only interpolation reconstruction but also extrapolation reconstruction. The proposed method does require determination of the Hurst exponent and other parameters analysed from the original data. Hence, the fluctuating wind fields have been characterized by the von Kármán correlation function, as an a priori function. Compared with the cubic spline method and different variogram, preliminary results suggest less time consumption and high efficiency in extrapolation reconstruction at the edge.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Quan Y, Liang Y, Wang F, Gu M. Wind tunnel test study on the wind pressure coefficient of claddings of high-rise buildings. Frontiers of Architecture and Civil Engineering in China, 2011, 5(4): 518–524

    Article  Google Scholar 

  2. Han D J, Li J. Application of proper orthogonal decomposition method in wind field simulation for roof structures. Journal of Engineering Mechanics, 2009, 135(8): 786–795

    Article  Google Scholar 

  3. Wang Y G, Li Z N, Li Q S, Gong B. Application of POD method on the wind-induced vibration response of heliostat. Journal Vibration and Shock, 2008, 27(12): 107–111 (in Chinese)

    Google Scholar 

  4. Zhou X Y, Li G. Application of POD combined with thin-plate splines in research on wind pressure. Building Structure, 2011, (06): 98–102 (in Chinese)

    Google Scholar 

  5. Cammelli S, Vacca L, Li Y F. The investigation of multi-variate random pressure fields acting on a tall building through proper orthogonal decomposition. International Association for Bridge and Structural Engineering Symposium Report, 2016: 897–904

    Google Scholar 

  6. Zhao Z W, Chen Z H, Wang X D, Hao X, Liu H B. Wind-induced response of large-span structures based on POD-pseudo-excitation method. Advanced Steel Construction, 2016, 12(1): 1–16

    Google Scholar 

  7. Fu J Y, Li Q S, Xie Z N. Prediction of wind loads on a large flat roof using fuzzy neural networks. Engineering Structures, 2006, 28(1): 153–161

    Article  Google Scholar 

  8. Fu J Y, Liang S G, Li Q S. Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks. Computers & Structures, 2007, 85(3–4): 179–192

    Article  Google Scholar 

  9. Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

    Article  Google Scholar 

  10. Armitt J. Eigenvector analysis of pressure fluctuations on the West Burton instrumented cooling tower. Internal Report RD/L/N 114/68, Central Electricity Research Laboratories UK, 1968

    Google Scholar 

  11. Berkooz G, Holmes P, Lumley J L. The proper orthogonal decomposition in the analysis of turbulent flows. Annual Review of Fluid Mechanics, 1993, 25(1): 539–575

    Article  MathSciNet  Google Scholar 

  12. Borée J. Extended proper orthogonal decomposition: a tool to analyse correlated events in turbulent flows. Experiments in Fluids, 2003, 35(2): 188–192

    Article  Google Scholar 

  13. Motlagh S Y, Taghizadeh S. POD analysis of low Reynolds turbulent porous channel flow. International Journal of Heat and Fluid Flow, 2016, 61: 665–676

    Article  Google Scholar 

  14. Kareem A, Cermak J E. Pressure fluctuations on a square building model in boundary-layer flows. Journal of Wind Engineering and Industrial Aerodynamics, 1984, 16(1): 17–41

    Article  Google Scholar 

  15. Holmes J D. Analysis and synthesis of pressure fluctuations on bluff bodies using eigenvectors. Journal of Wind Engineering and Industrial Aerodynamics, 1990, 33(1–2): 219–230

    Article  Google Scholar 

  16. Bienkiewicz B, Tamura Y, Ham H J, Ueda H, Hibi K. Proper orthogonal decomposition and reconstruction of multi-channel roof pressure. Journal of Wind Engineering and Industrial Aerodynamics, 1995, 54: 369–381

    Article  Google Scholar 

  17. Tamura Y, Suganuma S, Kikuchi H, Hibi K. Proper orthogonal decomposition of random wind pressure field. Journal of Fluids and Structures, 1999, 13(7–8): 1069–1095

    Article  Google Scholar 

  18. Uematsu Y, Kuribara O, Yamada M, Sasaki A, Hongo T. Windinduced dynamic behavior and its load estimation of a single-layer latticed dome with a long span. Journal of Wind Engineering and Industrial Aerodynamics, 2001, 89(14–15): 1671–1687

    Article  Google Scholar 

  19. Wang Y G, Li Z N, Gong B, Li Q S. Reconstruction & prediction of wind pressure on heliostat. Acta Aerodynamica Sinica, 2009, 27(5): 586–591 (in Chinese)

    Google Scholar 

  20. Jiang Z R, Ni Z H, Xie Z N. Reconstruction and prediction of wind pressure field on roof. Chinese Journal of Applied Mechanics, 2007, 24(4): 592–598 (in Chinese)

    Google Scholar 

  21. Li F H, Ni Z H, Shen S Z, Gu M. Theory of POD and its application in wind engineering of structure. Journal of Vibration and Shock, 2009, 28(4): 29–32 (in Chinese)

    Google Scholar 

  22. Li F H, Gu M, Ni Z H, Shen S Z. Wind pressures on structures by proper orthogonal decomposition. Journal of Civil Engineering and Architecture, 2012, 6(2): 238–243

    Google Scholar 

  23. Chen F B, Li Q S. Application investigation of predicting wind loads on large-span roof by Kriging-POD method. Engineering Mechanics, 2014, 31(1): 91–96 (in Chinese)

    Google Scholar 

  24. Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, (3): 1–13

    Google Scholar 

  25. Zhuang X, Huang R, Liang C, Rabczuk T. A coupled thermo-hydromechanical model of jointed hard rock for compressed air energy storage. Mathematical Problems in Engineering, 2014, 2014: 179169

    Google Scholar 

  26. Wang Y G, Li Z N, Wu H H, Zhang L H. Predication of fluctuating wind pressure on low building roof. Journal of Vibration and Shock, 2013, 32(5): 157–162 (in Chinese)

    Google Scholar 

  27. Loeve M. Probability theory II. Vol. 46, Graduate Texts in mathematics, 1978, 1–387

    Book  MATH  Google Scholar 

  28. Liang Y C, Lee H P, Lim S P, Lin W Z, Lee K H, Wu C G. Proper orthogonal decomposition and its applications—Part I: theory. Journal of Sound and Vibration, 2002, 252(3): 527–544

    Article  MathSciNet  MATH  Google Scholar 

  29. Matheron G. Principles of geostatistics. Economic Geology and the Bulletin of the Society of Economic Geologists, 1963, 58(8): 1246–1266

    Article  Google Scholar 

  30. Oliver M A, Webster R. Basic Steps in Geostatistics: the Variogram and Kriging. Springer International, 2015

    Google Scholar 

  31. Sarma D D. Geostatistics with Applications in Earth Sciences. Springer Science & Business Media, 2009, 265–269

    Book  MATH  Google Scholar 

  32. Von Kármán T. Progress in the statistical theory of turbulence. Proceedings of the National Academy of Sciences of the United States of America, 1948, 34(11): 530–539

    Article  MathSciNet  MATH  Google Scholar 

  33. Sidler R. Kriging and Conditional Geostatistical Simulation Based on Scale-Invariant Covariance Models. Swiss Federal Institute of Technology Zurich, 2003

    Google Scholar 

  34. Müller T M, Toms-Stewart J, Wenzlau F. Velocity-saturation relation for partially saturated rocks with fractal pore fluid distribution. Geophysical Research Letters, 2008, 35(9): L09306

    Article  Google Scholar 

  35. Guatteri M, Mai P M, Beroza G C. A pseudo-dynamic approximation to dynamic rupture models for strong ground motion prediction. Bulletin of the Seismological Society of America, 2004, 94(6): 2051–2063

    Article  Google Scholar 

  36. Cody W J. An overview of software development for special functions. In: Alistair Watson G, ed. Numerical Analysis: Proceedings of the Dundee Conference on Numerical Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1976, 38–48

    Book  MATH  Google Scholar 

  37. Abramowitz M, Stegun I A. Handbook of Mathematical Functions. National Bureau of Standards: Applied Math. Series #55: Dover Publications, 1965

    MATH  Google Scholar 

  38. Klimeš L. Correlation functions of random media. Pure and Applied Geophysics, 2002, 159(7): 1811–1831

    Google Scholar 

  39. Katsev S, L’Heureux I. Are Hurst exponents estimated from short or irregular time series meaningful? Computers & Geosciences, 2003, 29(9): 1085–1089

    Article  Google Scholar 

  40. Hurst H E. Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 1951, 116(1): 770–799

    Google Scholar 

  41. Aue A, Horváth L, Steinebach J. Rescaled range analysis in the presence of stochastic trend. Statistics & Probability Letters, 2007, 77(12): 1165–1175

    Article  MathSciNet  MATH  Google Scholar 

  42. Mason D M. The Hurst phenomenon and the rescaled range statistic. Stochastic Processes and Their Applications, 2016, 126(12): 3790–3807

    Article  MathSciNet  MATH  Google Scholar 

  43. Mandelbrot B B, Wallis J R. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, 1969, 5(5): 967–988

    Article  Google Scholar 

  44. Pardo-Igúzquiza E. MLREML: a computer program for the inference of spatial covariance parameters by maximum likelihood and restricted maximum likelihood. Computers & Geosciences, 1997, 23(2): 153–162

    Article  Google Scholar 

  45. Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Fundation of China (Grant No. 51469016), and its supports are gratefully acknowledged.

Author information

Authors and Affiliations

  1. School of Civil Engineering and Architecture, Nanchang University, Nanchang, 330033, China

    Yehua Sun, Guquan Song & Hui Lv

  2. Jiangxi Institute of Economic Administrators, Nanchang, 330038, China

    Hui Lv

Authors
  1. Yehua Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guquan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guquan Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Song, G. & Lv, H. Extrapolation reconstruction of wind pressure fields on the claddings of high-rise buildings. Front. Struct. Civ. Eng. 13, 653–666 (2019). https://doi.org/10.1007/s11709-018-0503-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11709-018-0503-5

Keywords

Search

Navigation