Abstract
The application of performance-based design (PBD) requires the modeling of the dynamic response of the system beyond the elastic limit. If probabilistic PBD is considered, this implies the need to propagate uncertainties through non-linear dynamic systems. This paper investigates the possibility of using advanced metamodeling techniques in order to define a computationally tractable approach for propagating uncertainty through a class of multi-degree-of-freedom non-linear dynamic systems subject to multivariate stochastic wind excitation. To this end, a scheme is introduced that is based on combining model order reduction with a recently introduced metamodeling approach that has been seen to be particularly effective in describing the dynamic response of uncertain non-linear systems of low dimensions. A case study consisting in a 40-story moment resisting frame subject to multivariate stochastic wind excitation and an array of non-linear viscous dampers is presented to illustrate the potential of the scheme.
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References
Spence SMJ, Gioffrè M (2012) Large scale reliability-based design optimization of wind excited tall buildings. Probab Eng Mech 28:206–215
Bernardini E, Spence SMJ, Kwon DK, Kareem A (2015) Performance-based design of high-rise buildings for occupant comfort. J Struct Eng. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001223
Tabbuso P, Spence SMJ, Palizzolo L, Pirrotta A, Kareem A (2016) An efficient framework for the elasto-plastic reliability assessment of uncertain wind excited systems. Struct Saf 58:69–78
Chuang WC, Spence SMJ (2017) A performance-based design framework for the integrated collapse and non-collapse assessment of wind excited buildings. Eng Struct 150:746–758
Gioffrè M, Gusella V (2007) Peak response of a nonlinear beam. J Eng Mech 133(9):963–969
Ciampoli M, Petrini F, Augusti G (2011) Performance-based wind engineering: towards a general procedure. Struct Saf 33(6):367–378
Petrini F, Ciampoli M (2012) Performance-based wind design of tall buildings. Struct Infrastruct Eng 8(10):954–966
Caracoglia L (2014) A stochastic model for examining along-wind loading uncertainty and intervention costs due to wind-induced damage on tall buildings. Eng Struct 78:121–132
Cui W, Caracoglia L (2015) Simulation and analysis of intervention costs due to wind-induced damage on tall buildings. Eng Struct 87:183–197
Cui W, Caracoglia L (2017) Exploring hurricane wind speed along us atlantic coast in warming climate and effects on predictions of structural damage and intervention costs. Eng Struct 122:209–225
Spiridonakos MD, Chatzi EN (2015) Metamodeling of dynamic nonlinear structural systems through polynomial chaos NARX models. Comput Struct 157:99–113
Mai CV, Spiridonakos MD, Chatzi EN, Sudret B (2016) Surrogate modelling for stochastic dynamical systems by combining narx models and polynomial chaos expansions. Int J Uncertain Quantif 6:313–339
Mai CV (2016) Polynomial chaos expansions for uncertain dynamical systems—applications in earthquake engineering. Ph.D. Thesis, ETH Zurich, Switzerland
Eman H, Pradlwarter HJ, Schuëller GI (2000) A computational procedure for the implementation of equivalent linearization in finite element analysis. Earthq Eng Struct Dyn 29:1–17
Pradlwarter HJ, Schuëller GI, Schenk CA (2003) A computational procedure to estimate the stochastic dynamic response of large non-linear FE-models. Comput Methods Appl Mech Eng 192:777–801
Schenk CA, Pradlwarter HJ, Schuëller GI (2004) On the dynamic stochastic response of FE models. Probab Eng Mech 19:161–170
Jensen HA, Catalan AA (2007) On the effects of non-linear elements in the reliability based optimal design of stochastic dynamical systems. Int J Non-Linear Mech 42:802–816
Valdebenito MA, Schuëller GI (2011) Efficient strategies for reliability-based optimization involving non-linear, dynamical structures. Comput Struct 89:1797–1811
Beck AT, Kougioumtzoglou IA, dos Santos KRM (2014) Optimal performance-based design of non-linear stochastic dynamical RC structures subject to stationary wind excitation. Eng Struct 78:145–153
Mitseas IP, Kougioumtzoglou IA, Beer M (2016) An approximate stochastic dynamics approach for nonlinear structural system performance-based multi-objective optimum design. Struct Saf 60:67–76
Wilson EL (2002) Three dimensional static and dynamic analysis of structures: a physical approach with emphasis on earthquake engineering. Computers and Structures Inc., Berkeley
Diniz SMC, Sadek F, Simiu E (2004) Wind speed estimation uncertainties: effects of climatological and micrometeorological parameters. Probab Eng Mech 19(4):361–371
Diniz SMC, Simiu E (2005) Probabilistic descriptions of wind effects and wind-load factors for database-assisted design. J Struct Eng 131(3):507–516
Bashor R, Kijewski-Correa T, Kareem A (2005) On the wind-induced response of tall buildings: the effects of uncertainties in dynamic properties and human comfort thresholds. In: Proceedings of the \(10^{{\rm th}}\) Americas conference on wind engineering
Billings SA (2013) Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley, New York
Billings SA, Wei HL (2008) An adaptive orthogonal search algorithm for model subset selection and non-linear system identification. Int J Control 81(5):714–724
Wei HL, Billings SA (2008) Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information. Int J Model Identif Control 3(4):341–356
Blatman G, Sudret B (2011) Adaptive sparse polynomial chaos expansion based on least angle regression. J Comput Phys 230:2345–2367
Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optim 9(1):112–147
Symans MD, Constantinou MC (1998) Passive fluid viscous damping systems for seismic energy dissipation. J Earthq Technol 35(4):185–206
Deodatis G (1996) Simulation of ergodic multivariate stochastic processes. J Eng Mech 122:778–787
Kaimal JC, Wyngaard JC, Izumi Y, Coteé OR (1972) Spectral characteristics of surface-layer turbulence. Q J R Meteorol Soc 98(417):563–589
Davenport GA (1967) The dependence of wind load upon meteorological parameters. In: Proceedings of the international research seminar on wind effects on building and structures. University of Toronto Press, pp 19–82
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This research effort was supported in part by the National Science Foundation (NSF) under Grant No. CMMI-1750339. This support is gratefully acknowledged.
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Appendix 1: Target wind spectrum
Appendix 1: Target wind spectrum
The target power spectral density (PSD) function of the fluctuating wind velocity, \(v_{z_j}(t)\), can be taken as [32]:
where \(v_*\) is the shear velocity given by:
where \({\bar{v}}_{10}\) is the mean wind velocity at 10 m, β = 0.65, while \(k_a = 0.4\) is the Von Kármán’s constant. The cross power spectral density can then be defined as:
where \(\gamma _{jk}\) is the coherence function between \(v_{v_{z_j}}(t)\) and \(v_{v_{z_j}}(t)\) that can be modeled as [33]:
where \({\varDelta } z= | z_j - z_k |\) is the height difference, while \(C_z\) is a constant that can be set equal to 10 for design purposes [33].
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Chuang, WC., Spence, S.M.J. Rapid uncertainty quantification for non-linear and stochastic wind excited structures: a metamodeling approach. Meccanica 54, 1327–1338 (2019). https://doi.org/10.1007/s11012-019-00958-9
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DOI: https://doi.org/10.1007/s11012-019-00958-9