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http://dx.doi.org/10.12989/sem.2009.32.1.037

Reliability analysis of wind-excited structures using domain decomposition method and line sampling  

Katafygiotis, L.S. (Department of Civil Engineering, Hong Kong University of Science and Technology)
Wang, Jia (Department of Civil Engineering, Hong Kong University of Science and Technology)
Publication Information
Structural Engineering and Mechanics / v.32, no.1, 2009 , pp. 37-53 More about this Journal
Abstract
In this paper the problem of calculating the probability that the responses of a wind-excited structure exceed specified thresholds within a given time interval is considered. The failure domain of the problem can be expressed as a union of elementary failure domains whose boundaries are of quadratic form. The Domain Decomposition Method (DDM) is employed, after being appropriately extended, to solve this problem. The probability estimate of the overall failure domain is given by the sum of the probabilities of the elementary failure domains multiplied by a reduction factor accounting for the overlapping degree of the different elementary failure domains. The DDM is extended with the help of Line Sampling (LS), from its original presentation where the boundary of the elementary failure domains are of linear form, to the current case involving quadratic elementary failure domains. An example involving an along-wind excited steel building shows the accuracy and efficiency of the proposed methodology as compared with that obtained using standard Monte Carlo simulations (MCS).
Keywords
reliability analysis; wind excitation; domain decomposition method; line sampling;
Citations & Related Records

Times Cited By Web Of Science : 2  (Related Records In Web of Science)
Times Cited By SCOPUS : 3
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1 Au, S.K. and Beck, J.L. (2001), "First excursion probabilities for linear systems by very efficient importance sampling", Probabilist. Eng. Mech., 16(3), 193-207   DOI   ScienceOn
2 Au, S.K. and Beck, J.L. (2003), "Importance sampling in high dimensions", Struct. Safety, 25(2), 139-163   DOI   ScienceOn
3 Brigham, E.O. (1988), The Fast Fourier Transform and its Applications, Prentice-Hall, Inc., Englewood Cliffs, New Jersey
4 Davenport, A.G. (1961), "The spectrum of horizontal gustiness near the ground in high winds", J. R. Meteorol. Soc., 87, 194-211   DOI
5 Davenport, A.G. (1968), "The dependence of wind load upon meteorological parameters", Proceedings of the International Research Seminar on Wind Effects on Buildings and Structures, Toronto
6 Deodatis, G. (1996), "Simulation of ergodic multivariate stochastic processes", J. Eng. Mech., 122(8), 778-787   DOI   ScienceOn
7 Di Paola, M. and Gullo, I. (2001) "Digital generation of multivariate wind field processes", Probabilist. Eng. Mech., 16, 1-10   DOI   ScienceOn
8 Grigoriu, M. (2002), Stochastic Calculus: Applications in Science and Engineering, Birkhauser Boston, New York
9 Jacob, B. (1990), Linear Algebra, W.H. Freeman and Company, New York
10 Katafygiotis, L.S. and Cheung, S.H. (2006), "Domain decomposition method for calculating the failure probability of linear dynamic systems subjected to Gaussian stochastic loads", J. Eng. Mech., 132(5), 475-486   DOI   ScienceOn
11 Katafygiotis, L.S. and Zuev, K.M. (2008), "Geometric insight to the challenges of solving high-dimensional reliability problems", Probabilist. Eng. Mech., 23(2-3), 208-218   DOI   ScienceOn
12 Koutsourelakis, P.S., Pradlwarter, H.J. and Schuëller, G.I. (2004), "Reliability of structures in high dimensions, part I: algorithms and applications", Probabilist. Eng. Mech., 19(4), 409-417   DOI   ScienceOn
13 Melbourne, W.H. (1980), "Comparison of measurements on the CAARC standard tall building model in simulated model wind flows", J. Wind. Eng. Ind. Aerod., 6, 73-88   DOI   ScienceOn
14 Pradlwarter, H.J., Schu$\ddot{e}$ller, G.I., Koutsourelakis, P.S. and Charmpis, D.C. (2007), "Application of line sampling simulation method to reliability benchmark problems", Struct. Safety, 29, 208-221   DOI   ScienceOn
15 Proppe, C., Pradlwarter, H.J. and Schu$\ddot{e}$ller, G.I. (2003) "Equivalent linearization and Monte Carlo simulations in stochastic dynamics", Probabilist. Eng. Mech., 18(1), 1-15   DOI   ScienceOn
16 Rubinstein, R.Y. (1981), Simulation and the Monte-Carlo Method, New York: Wiley
17 Schu$\ddot{e}$ller, G.I., Pradlwarter, H.J. and Koutsourelakis, P.S. (2003), "A comparative study of reliability estimation procedures for high dimensions", 16th ASCE Engineering Mechanics Conference, University of Washington, Seattle
18 Schu$\ddot{e}$ller, G.I., Pradlwarter, H.J. and Koutsourelakis, P.S. (2004), "A critical appraisal of reliability estimation procedures for high dimensions", Probabilist. Eng. Mech., 19(4), 463-474   DOI   ScienceOn
19 Shinozuka, M. and Jan, C.M. (1972), "Digital simulation of random processes and its applications", J. Sound Vib., 25(1), 111-128   DOI   ScienceOn
20 Schu$\ddot{e}$ller, G.I. and Pradlwarter, H.J. (2007), "Benchmark study on reliability estimation in higher dimensions of structural systems – An overview", Struct. Safety, 29(3), 167-182   DOI   ScienceOn
21 Shinozuka, M. and Deodatis, G. (1991), "Simulation of stochastic processes by spectral representation", Appl. Mech. Rev., 44(4), 191-204   DOI
22 Simiu, E. and Scanlan, R.H. (1986), Wind Effects on Structures, John Wiley & Sons, Inc., New York
23 Yuen, K.V. and Katafygiotis, L.S. (2004), "An efficient simulation method for reliability analysis using simple additive rules of probability", Probabilist. Eng. Mech., 20(1), 109-114   DOI   ScienceOn