Clustering and Selection of Hurricane Wind Records Using Autoencoder and -Means Algorithm
Publication: Journal of Structural Engineering
Volume 149, Issue 8
Abstract
In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is of great interest. Such a suite generally consists of a large number of hurricane wind records, which may lead to highly computational cost for structural analysis. To reduce the computational demand while still preserving the accuracy of the uncertainty quantification process, this paper proposes a machine learning approach to select a representative subset of all collected hurricane wind records for a location. First, hurricane wind records, which are expressed as time series with information that includes both wind speed and direction, are collected from a synthetic hurricane catalog. The high dimensional hurricane wind records are then compressed into a set of low dimensional latent feature vectors using an artificial neural network, designated as an autoencoder. The latent feature vectors represent the important patterns of wind records such as duration, magnitude, and the changing of wind speeds and directions over time. The wind records are then clustered by applying the -means algorithm on the latent features, and a subset of records is selected from each cluster. The wind records selected from each cluster are those whose latent feature points are closest to the centroid of all latent feature points in that cluster. In order to do regional analysis while taking into account that the hurricane wind records are site-specific, this paper suggests that a region can be discretized into a set of grids, with the proposed hurricane selection approach applied to each grid. This procedure is demonstrated using Massachusetts as a testbed.
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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. Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors wish to thank Dr. Weichiang Pang of Clemson University for providing the synthetic hurricane catalog. The material presented in this paper is based upon work supported by National Science Foundation under Grant No. CRISP-1638234 and Northeastern University. This support is gratefully acknowledged.
References
Aggarwal, C. C. 2018. Neural networks and deep learning. New York: Springer.
Aggarwal, C. C., A. Hinneburg, and D. A. Keim. 2001. “On the surprising behavior of distance metrics in high dimensional space.” In Proc., Int. Conf. on Database Theory, 420–434. Berlin: Springer.
ASCE. 2016a. “ASCE 7 hazard tool.” Accessed October 17, 2021. https://asce7hazardtool.online/.
ASCE. 2016b. Minimum design loads and associated criteria for buildings and other structures (ASCE Standard 7-16). Reston, VA: ASCE.
ASCE. 2019. Prestandard for performance–based wind design. Reston, VA: ASCE.
Baker, J. W., and C. Lee. 2018. “An improved algorithm for selecting ground motions to match a conditional spectrum.” J. Earthquake Eng. 22 (4): 708–723. https://doi.org/10.1080/13632469.2016.1264334.
Barbato, M., F. Petrini, V. U. Unnikrishnan, and M. Ciampoli. 2013. “Performance-based hurricane engineering (PBHE) framework.” Struct. Saf. 45 (Jun): 24–35. https://doi.org/10.1016/j.strusafe.2013.07.002.
Batts, M. E., M. R. Cordes, L. R. Russell, J. R. Shaver, and E. Simiu. 1980. Hurricane wind speeds in the United States. Gaithersburg, MD: National Bureau of Standards.
Beyer, K., J. Goldstein, R. Ramakrishnan, and U. Shaft. 1999. “When is “nearest neighbor” meaningful?” In Proc., Int. Conf. on Database Theory, 217–235. Berlin: Springer.
Bojórquez, E., A. Reyes-Salazar, S. E. Ruiz, and J. Bojórquez. 2013. “A new spectral shape-based record selection approach using Np and genetic algorithms.” Math. Probl. Eng. 2013 (1): 679026. https://doi.org/10.1155/2013/679026.
Bond, R. B., P. Ren, J. F. Hajjar, and H. Sun. 2022. “An unsupervised machine learning approach for ground motion clustering and selection.” Preprint, submitted December 6, 2022. https://arxiv.org/abs/2212.03188.
Botev, Z., and A. Ridder. 2017. Variance reduction, 1–6. New York: Wiley. https://doi.org/10.1002/9781118445112.stat07975.
Chuang, W.-C., and S. M. Spence. 2019. “An efficient framework for the inelastic performance assessment of structural systems subject to stochastic wind loads.” Eng. Struct. 179 (Jun): 92–105. https://doi.org/10.1016/j.engstruct.2018.10.039.
Chuang, W.-C., and S. M. Spence. 2020. “Probabilistic performance assessment of inelastic wind excited structures within the setting of distributed plasticity.” Struct. Saf. 84 (May): 101923. https://doi.org/10.1016/j.strusafe.2020.101923.
Cui, W., and L. Caracoglia. 2015. “Simulation and analysis of intervention costs due to wind-induced damage on tall buildings.” Eng. Struct. 87 (Mar): 183–197. https://doi.org/10.1016/j.engstruct.2015.01.001.
Cui, W., and L. Caracoglia. 2019. “A new stochastic formulation for synthetic hurricane simulation over the North Atlantic Ocean.” Eng. Struct. 199 (Nov): 109597. https://doi.org/10.1016/j.engstruct.2019.109597.
Der Kiureghian, A. 2005. “First-and second-order reliability methods.” In Engineering design reliability handbook, edited by E. Nikolaidis, D. Ghiocel, and S. Singhal. Boca Raton, FL: CRC Press.
Du, A., and J. E. Padgett. 2021. “Refined multivariate return period-based ground motion selection and implications for seismic risk assessment.” Struct. Saf. 91 (Jun): 102079. https://doi.org/10.1016/j.strusafe.2021.102079.
Du, X., and J. Hajjar. 2022. Event-based collapse fragility development of electrical transmission towers for regional hurricane risk analysis. Washington, DC: Engineering Archive. https://doi.org/10.31224/2661.
FEMA. 2009. Quantification of building seismic performance factors. Washington, DC: FEMA.
Georgiou, P. N. 1985. “Design wind speeds in tropical cyclone-prone regions.” Ph.D. dissertation, Faculty of Graduate Studies, Univ. of Western Ontario.
Hallowell, S. T., A. T. Myers, S. R. Arwade, W. Pang, P. Rawal, E. M. Hines, J. F. Hajjar, C. Qiao, V. Valamanesh, and K. Wei. 2018. “Hurricane risk assessment of offshore wind turbines.” Renewable Energy 125 (Jun): 234–249. https://doi.org/10.1016/j.renene.2018.02.090.
Holland, G. J. 1980. “An analytic model of the wind and pressure profiles in hurricanes.” Mon. Weather Rev. 108 (8): 1212–1218. https://doi.org/10.1175/1520-0493(1980)108%3C1212:AAMOTW%3E2.0.CO;2.
Jarvinen, B. R., C. J. Neumann, and M. A. Davis. 1984. A tropical cyclone data tape for the North Atlantic Basin, 1886-1983: Contents, limitations, and uses. Washington, DC: US Department of Commerce.
Jayaram, N., T. Lin, and J. W. Baker. 2011. “A computationally efficient ground-motion selection algorithm for matching a target response spectrum mean and variance.” Earthquake Spectra 27 (3): 797–815. https://doi.org/10.1193/1.3608002.
Joyner, M. D., and M. Sasani. 2018. “Multihazard risk-based resilience analysis of east and west coast buildings designed to current codes.” J. Struct. Eng. 144 (9): 04018156. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002132.
Kim, S.-M., S.-Y. Ok, and J. Song. 2019. “Multi-scale dynamic system reliability analysis of actively-controlled structures under random stationary ground motions.” KSCE J. Civ. Eng. 23 (3): 1259–1270. https://doi.org/10.1007/s12205-019-1584-y.
Kim, T., O. S. Kwon, and J. Song. 2021. “Clustering-based adaptive ground motion selection algorithm for efficient estimation of structural fragilities.” Earthquake Eng. Struct. Dyn. 50 (6): 1755–1776. https://doi.org/10.1002/eqe.3418.
Kramer, M. A. 1991. “Nonlinear principal component analysis using autoassociative neural networks.” AIChE J. 37 (2): 233–243. https://doi.org/10.1002/aic.690370209.
Krawinkler, H., R. Medina, and B. Alavi. 2003. “Seismic drift and ductility demands and their dependence on ground motions.” Eng. Struct. 25 (5): 637–653. https://doi.org/10.1016/S0141-0296(02)00174-8.
Li, Y. 2005. “Fragility methodology for performance-based engineering of wood-frame residential construction.” Ph.D. dissertation, School of Civil and Environmental Engineering, Georgia Institute of Technology.
Li, Y., and B. R. Ellingwood. 2006. “Hurricane damage to residential construction in the US: Importance of uncertainty modeling in risk assessment.” Eng. Struct. 28 (7): 1009–1018. https://doi.org/10.1016/j.engstruct.2005.11.005.
Liu, F. 2014. “Projections of future US design wind speeds and hurricane losses due to climate change.” Ph.D. dissertation, Dept. of Civil Engineering, Clemson Univ.
Ma, L., M. Khazaali, and P. Bocchini. 2021. “Component-based fragility analysis of transmission towers subjected to hurricane wind load.” Eng. Struct. 242 (Aug): 112586. https://doi.org/10.1016/j.engstruct.2021.112586.
Moehle, J., and G. G. Deierlein. 2004. “A framework methodology for performance-based earthquake engineering.” In Proc., 13th World Conf. on Earthquake Engineering. Vancouver, BC, Canada: Canadian Association for Earthquake Engineering.
Naeim, F., A. Alimoradi, and S. Pezeshk. 2004. “Selection and scaling of ground motion time histories for structural design using genetic algorithms.” Earthquake spectra 20 (2): 413–426. https://doi.org/10.1193/1.1719028.
Parsons, V. L. 2014. Stratified sampling, 1–11. New York: Wiley. https://doi.org/10.1002/9781118445112.stat05999.
Pearson, K. 1901. “LIII. On lines and planes of closest fit to systems of points in space.” London, Edinburgh, Dublin Philos. Mag. J. Sci. 2 (11): 559–572. https://doi.org/10.1080/14786440109462720.
Pei, B., W. Pang, F. Y. Testik, N. Ravichandran, and F. Liu. 2014. “Mapping joint hurricane wind and surge hazards for Charleston, South Carolina.” Nat. Hazards 74 (2): 375–403. https://doi.org/10.1007/s11069-014-1185-5.
Pei, B., W. Pang, F. Y. Testik, N. Ravichandran, and F. Liu. 2018. “Selection of hazard-consistent hurricane scenarios for regional combined hurricane wind and flood loss estimation.” Nat. Hazards 91 (2): 671–696. https://doi.org/10.1007/s11069-017-3149-z.
Roweis, S. T., and L. K. Saul. 2000. “Nonlinear dimensionality reduction by locally linear embedding.” Science 290 (5500): 2323–2326. https://doi.org/10.1126/science.290.5500.2323.
Russell, L. R. 1971. “Probability distributions for hurricane effects.” J. Waterways Harbors Coastal Eng. Div. 97 (1): 139–154. https://doi.org/10.1061/AWHCAR.0000056.
Shalev-Shwartz, S., and S. Ben-David. 2014. Understanding machine learning: From theory to algorithms. New York: Cambridge University Press.
Simiu, E., and R. H. Scanlan. 1996. Wind effects on structures: Fundamentals and applications to design. 3rd ed. New York: Wiley.
Somerville, P., N. Smith, S. Punyamurthula, and J. Sun. 1997. Development of ground motion time histories for phase 2 of the FEMA/SAC steel project. Richmond, CA: SAC Joint Venture.
Straub, D., R. Schneider, E. Bismut, and H.-J. Kim. 2020. “Reliability analysis of deteriorating structural systems.” Struct. Saf. 82 (Jun): 101877. https://doi.org/10.1016/j.strusafe.2019.101877.
Tabbuso, P., S. M. Spence, L. Palizzolo, A. Pirrotta, and A. Kareem. 2016. “An efficient framework for the elasto-plastic reliability assessment of uncertain wind excited systems.” Struct. Saf. 58 (Jan): 69–78. https://doi.org/10.1016/j.strusafe.2015.09.001.
Tavakoli, N., S. Siami-Namini, M. A. Khanghah, F. M. Soltani, and A. S. Namin. 2020. “An autoencoder-based deep learning approach for clustering time series data.” SN Appl. Sci. 2 (5): 1–25. https://doi.org/10.1007/s42452-020-2584-8.
Thorndike, R. L. 1953. “Who belongs in the family?” Psychometrika 18 (4): 267–276. https://doi.org/10.1007/BF02289263.
Vamvatsikos, D., and C. A. Cornell. 2002. “Incremental dynamic analysis.” Earthquake Eng. Struct. Dyn. 31 (3): 491–514. https://doi.org/10.1002/eqe.141.
Vickery, P., P. Skerlj, and L. Twisdale. 2000a. “Simulation of hurricane risk in the US using empirical track model.” J. Struct. Eng. 126 (10): 1222–1237. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:10(1222).
Vickery, P. J., F. J. Masters, M. D. Powell, and D. Wadhera. 2009a. “Hurricane hazard modeling: The past, present, and future.” J. Wind Eng. Ind. Aerodyn. 97 (7–8): 392–405. https://doi.org/10.1016/j.jweia.2009.05.005.
Vickery, P. J., P. Skerlj, A. Steckley, and L. Twisdale. 2000b. “Hurricane wind field model for use in hurricane simulations.” J. Struct. Eng. 126 (10): 1203–1221. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:10(1203).
Vickery, P. J., P. F. Skerlj, J. Lin, L. A. Twisdale Jr., M. A. Young, and F. M. Lavelle. 2006. “HAZUS-MH hurricane model methodology. II: Damage and loss estimation.” Nat. Hazard. Rev. 7 (2): 94–103. https://doi.org/10.1061/(ASCE)1527-6988(2006)7:2(94).
Vickery, P. J., D. Wadhera, J. Galsworthy, J. A. Peterka, P. A. Irwin, and L. A. Griffis. 2010. “Ultimate wind load design gust wind speeds in the United States for use in ASCE-7.” J. Struct. Eng. 136 (5): 613–625. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000145.
Vickery, P. J., D. Wadhera, M. D. Powell, and Y. Chen. 2009b. “A hurricane boundary layer and wind field model for use in engineering applications.” J. Appl. Meteorol. Climatol. 48 (2): 381–405. https://doi.org/10.1175/2008JAMC1841.1.
Vickery, P. J., D. Wadhera, L. A. Twisdale Jr, and F. M. Lavelle. 2009c. “US hurricane wind speed risk and uncertainty.” J. Struct. Eng. 135 (3): 301–320. https://doi.org/10.1061/(ASCE)0733-9445(2009)135:3(301).
Wang, H., and T. Wu. 2022. “Statistical investigation of wind duration using a refined hurricane track model.” J. Wind Eng. Ind. Aerodyn. 221 (Aug): 104908. https://doi.org/10.1016/j.jweia.2022.104908.
Wold, S., K. Esbensen, and P. Geladi. 1987. “Principal component analysis.” Chemometr. Intell. Lab. Syst. 2 (1–3): 37–52. https://doi.org/10.1016/0169-7439(87)80084-9.
Zhang, R., J. Hajjar, and H. Sun. 2020. “Machine learning approach for sequence clustering with applications to ground-motion selection.” J. Eng. Mech. 146 (6): 04020040. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001766.
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Published In
Journal of Structural Engineering
Volume 149 • Issue 8 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 28, 2022
Accepted: Mar 9, 2023
Published online: May 19, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 19, 2023
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