Data-Driven Approach for Generating Tricomponent Nonstationary Non-Gaussian Thunderstorm Wind Records Using Continuous Wavelet Transforms and S-Transform
Publication: Journal of Structural Engineering
Volume 149, Issue 12
Abstract
Strong thunderstorm winds cause damage to structures. However, the available number of the tricomponent thunderstorm wind record with a subsecond sampling time interval is limited. In the present study, a record-based procedure for generating tricomponent nonstationary non-Gaussian thunderstorm wind records was proposed. The procedure was based on the iterative power and amplitude correction algorithm framework but with modifications. The modifications were aimed at increasing the variability of the sampled record components by randomizing the power spectral density functions of processes through a digital filter in the frequency domain and improving the convergence by using a relaxation factor for the synchronized phase shift. The formulation and algorithm for the proposed procedure were given by considering the continuous wavelet transform with the harmonic wavelet and generalized Morse wavelet, and the generalized S-transform, which can provide good time localized resolution at high frequencies (low scales) and good resolution at low frequencies (high scales) simultaneously. The proposed procedure, unlike some of the algorithms available in the literature, matches the marginal mixture cumulative distributions of the seed record components and does not require the separation of low- and high-frequency wind components. The use of the proposed procedure to sample tricomponent thunderstorm wind records was shown.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We gratefully acknowledge the financial support received from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2016-04814, for HPH). We thank Dr. M. Burlando for providing us with the thunderstorm wind records.
References
Brusco, S., G. Buresti, and G. Piccardo. 2022. “Thunderstorm-induced mean wind velocities and accelerations through the continuous wavelet transform.” J. Wind Eng. Ind. Aerodyn. 221 (Feb): 104886. https://doi.org/10.1016/j.jweia.2021.104886.
Burlando, M., S. Zhang, and G. Solari. 2018. “Monitoring, cataloguing, and weather scenarios of thunderstorm outflows in the northern Mediterranean.” Nat. Hazards Earth Syst. Sci. 18 (9): 2309–2330. https://doi.org/10.5194/nhess-18-2309-2018.
Chen, L., and C. W. Letchford. 2007. “Numerical simulation of extreme winds from thunderstorm downbursts.” J. Wind Eng. Ind. Aerodyn. 95 (9–11): 977–990. https://doi.org/10.1016/j.jweia.2007.01.021.
Choi, E. C. C., and F. A. Hidayat. 2002. “Dynamic response of structures to thunderstorm winds.” Prog. Struct. Eng. Mater. 4 (4): 408–416. https://doi.org/10.1002/pse.132.
Cohen, E. A. K., and A. T. Walden. 2010. “A statistical study of temporally smoothed wavelet coherence.” IEEE Trans. Signal Process. 58 (6): 2964–2973. https://doi.org/10.1109/TSP.2010.2043139.
Cui, X. Z., and H. P. Hong. 2021. “Simulating nonstationary and non-Gaussian vector ground motions with time- and frequency-dependent lagged coherence.” Earthquake Eng. Struct. Dyn. 50 (9): 2421–2441. https://doi.org/10.1002/eqe.3453.
Cui, X. Z., and H. P. Hong. 2022. “Decomposing seismic accelerograms with optimized window and its application for generating artificial fully non-Gaussian and nonstationary ground motion time histories.” Soil Dyn. Earthquake Eng. 154 (Mar): 107124. https://doi.org/10.1016/j.soildyn.2021.107124.
Daubechies, I. 1992. Vol. 61 of Ten lectures on wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics.
Dolan, K. T., and M. L. Spano. 2001. “Surrogate for nonlinear time series analysis.” Phys. Rev. E 64 (4): 046128. https://doi.org/10.1103/PhysRevE.64.046128.
Gu, P., and Y. K. Wen. 2007. “A record-based method for the generation of tridirectional uniform hazard-response spectra and ground motions using the Hilbert-Huang transform.” Bull. Seismol. Soc. Am. 97 (5): 1539–1556. https://doi.org/10.1785/0120060127.
Hong, H. P. 2021. “Response and first passage probability of linear elastic SDOF systems subjected to nonstationary stochastic excitation modelled through S-transform.” Struct. Saf. 88 (Jan): 102007. https://doi.org/10.1016/j.strusafe.2020.102007.
Hong, H. P., X. Z. Cui, and D. Qiao. 2021a. “An algorithm to simulate nonstationary and non-Gaussian stochastic processes.” J. Infrastruct. Preserv. Resilience 2 (1): 17. https://doi.org/10.1186/s43065-021-00030-5.
Hong, H. P., X. Z. Cui, and D. Qiao. 2022. “Simulating nonstationary non-Gaussian vector process based on continuous wavelet transform.” Mech. Syst. Signal Process. 165 (Feb): 108340. https://doi.org/10.1016/j.ymssp.2021.108340.
Hong, H. P., X. Z. Cui, and M. Y. Xiao. 2021b. “Modelling and simulating thunderstorm/downburst winds using S-transform and discrete orthonormal S-transform.” J. Wind Eng. Ind. Aerodyn. 212 (May): 104598. https://doi.org/10.1016/j.jweia.2021.104598.
Hong, H. P., Q. Tang, S. C. Yang, X. Z. Cui, A. J. Cannon, Z. Lounis, and P. Irwin. 2021c. “Calibration of the design wind load and snow load considering the historical climate statistics and climate change effects.” Struct. Saf. 93 (Nov): 102135. https://doi.org/10.1016/j.strusafe.2021.102135.
Huang, G., Y. Jiang, L. Peng, G. Solari, H. Liao, and M. Li. 2019. “Characteristics of intense winds in mountain area based on field measurement: Focusing on thunderstorm winds.” J. Wind Eng. Ind. Aerodyn. 190 (Jul): 166–182. https://doi.org/10.1016/j.jweia.2019.04.020.
Huang, G., L. Peng, A. Kareem, and C. Song. 2020. “Data-driven simulation of multivariate nonstationary winds: A hybrid multivariate empirical mode decomposition and spectral representation method.” J. Wind Eng. Ind. Aerodyn. 197 (Feb): 104073. https://doi.org/10.1016/j.jweia.2019.104073.
Kwon, D. K., and A. Kareem. 2019. “Towards codification of thunderstorm/downburst using gust front factor: Model-based and data-driven perspectives.” Eng. Struct. 199 (Nov): 109608. https://doi.org/10.1016/j.engstruct.2019.109608.
Li, C., K. Luo, and L. Cao. 2022. “Data-driven simulation of multivariate nonstationary wind velocity with explicit introduction of the time-varying coherence functions.” J. Wind Eng. Ind. Aerodyn. 220 (Jan): 104872. https://doi.org/10.1016/j.jweia.2021.104872.
Lilly, J. M., and S. C. Olhede. 2012. “Generalized Morse wavelets as a superfamily of analytic wavelets.” IEEE Trans. Signal Process. 60 (11): 6036–6041. https://doi.org/10.1109/TSP.2012.2210890.
Lombardo, F. T., J. A. Main, and E. Simiu. 2009. “Automated extraction and classification of thunderstorm and non-thunderstorm wind data for extreme-value analysis.” J. Wind Eng. Ind. Aerodyn. 97 (3–4): 120–131. https://doi.org/10.1016/j.jweia.2009.03.001.
Lombardo, F. T., D. A. Smith, J. L. Schroeder, and K. C. Mehta. 2014. “Thunderstorm characteristics of importance to wind engineering.” J. Wind Eng. Ind. Aerodyn. 125 (Feb): 121–132. https://doi.org/10.1016/j.jweia.2013.12.004.
Newland, D. E. 2012. An introduction to random vibrations, spectral & wavelet analysis. Mineola, NY: Courier Corporation.
Olhede, S. C., and A. T. Walden. 2002. “Generalized Morse wavelets.” IEEE Trans. Signal Process. 50 (11): 2661–2670. https://doi.org/10.1109/TSP.2002.804066.
Orwig, K. D., and J. L. Schroeder. 2007. “Near-surface wind characteristics of extreme thunderstorm outflows.” J. Wind Eng. Ind. Aerodyn. 95 (7): 565–584. https://doi.org/10.1016/j.jweia.2006.12.002.
Percival, D. B., and A. T. Walden. 2000. Wavelet methods for time series analysis. New York: Cambridge University Press.
Pinnegar, C. R., and L. Mansinha. 2003. “The S-transform with windows of arbitrary and varying shape.” Geophysics 68 (1): 381–385. https://doi.org/10.1190/1.1543223.
Schreiber, T., and A. Schmitz. 2000. “Surrogate time series.” Physica D 142 (3–4): 346–382. https://doi.org/10.1016/S0167-2789(00)00043-9.
Solari, G., P. De Gaetano, and M. P. Repetto. 2015. “Thunderstorm response spectrum: Fundamentals and case study.” J. Wind Eng. Ind. Aerodyn. 143 (Aug): 62–77. https://doi.org/10.1016/j.jweia.2015.04.009.
Stockwell, R. G. 2007. “A basis for efficient representation of the S-transform.” Digital Signal Process. 17 (1): 371–393. https://doi.org/10.1016/j.dsp.2006.04.006.
Stockwell, R. G., L. Mansinha, and R. P. Lowe. 1996. “Localization of the complex spectrum: The S transform.” IEEE Trans. Signal Process. 44 (4): 998–1001. https://doi.org/10.1109/78.492555.
Su, Y., G. Huang, and Y.-L. Xu. 2015. “Derivation of time-varying mean for non-stationary downburst winds.” J. Wind Eng. Ind. Aerodyn. 141 (Jun): 39–48. https://doi.org/10.1016/j.jweia.2015.02.008.
Torrence, C., and G. P. Compo. 1998. “A practical guide to wavelet analysis.” Bull. Am. Meteorol. Soc. 79 (1): 61–78. https://doi.org/10.1175/1520-0477(1998)079%3C0061:APGTWA%3E2.0.CO;2.
Tubino, F., and G. Solari. 2020. “Time varying mean extraction for stationary and nonstationary winds.” J. Wind Eng. Ind. Aerodyn. 203 (Aug): 104187. https://doi.org/10.1016/j.jweia.2020.104187.
Ventosa, S., C. Simon, M. Schimmel, J. J. Dañobeitia, and A. Mànuel. 2008. “The S-transform from a wavelet point of view.” IEEE Trans. Signal Process. 56 (7): 2771–2780.
Wang, H., and T. Wu. 2018. “Hilbert-wavelet-based nonstationary wind field simulation: A multiscale spatial correlation scheme.” J. Eng. Mech. 144 (8): 04018063. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001490.
Wang, L., M. McCullough, and A. Kareem. 2013. “A data-driven approach for simulation of full-scale downburst wind speeds.” J. Wind Eng. Ind. Aerodyn. 123 (Dec): 171–190. https://doi.org/10.1016/j.jweia.2013.08.010.
Xiao, M. Y., and H. P. Hong. 2022. “Modeling nonstationary non-Gaussian hurricane wind velocity and gust factor.” J. Struct. Eng. 148 (2): 04021263. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003243.
Xiao, M. Y., S. K. Li, and H. P. Hong. 2022. “Use of S-transform and a new multi-taper S-transform for spectral and coherence estimates: With application to characterize and simulate multivariate non-Gaussian wind pressure coefficient.” J. Wind Eng. Ind. Aerodyn. 230 (Nov): 105198. https://doi.org/10.1016/j.jweia.2022.105198.
Zhang, S., G. Solari, M. Burlando, and Q. Yang. 2019. “Directional decomposition and properties of thunderstorm outflows.” J. Wind Eng. Ind. Aerodyn. 189 (Jun): 71–90. https://doi.org/10.1016/j.jweia.2019.03.014.
Zhang, S., G. Solari, P. De Gaetano, M. Burlando, and M. P. Repetto. 2018. “A refined analysis of thunderstorm outflow characteristics relevant to the wind loading of structures.” Probab. Eng. Mech. 54 (Oct): 9–24. https://doi.org/10.1016/j.probengmech.2017.06.003.
Zhao, H., M. Grigoriu, and K. R. Gurley. 2019. “Translation processes for wind pressures on low-rise buildings.” J. Wind Eng. Ind. Aerodyn. 184 (Jan): 405–416. https://doi.org/10.1016/j.jweia.2018.12.007.
Zhao, S., Y. Ge, and G. Kopp. 2022. “Assessment of gust factors and wind speed decomposition methods for thunderstorms.” J. Wind Eng. Ind. Aerodyn. 223 (Apr): 104953. https://doi.org/10.1016/j.jweia.2022.104953.
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Information
Published In
Journal of Structural Engineering
Volume 149 • Issue 12 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 6, 2022
Accepted: Jun 12, 2023
Published online: Oct 4, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 4, 2024
ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Climates
- Continuous structures
- Continuum mechanics
- Drop structures
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Frequency response
- Gaussian process
- Mathematical functions
- Mathematics
- Meteorology
- Motion (dynamics)
- Natural frequency
- Oscillations
- Power spectral density
- Precipitation
- Probability
- Solid mechanics
- Statistical analysis (by type)
- Stochastic processes
- Storms
- Structural engineering
- Structures (by type)
- Wavelets
- Wind engineering
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