Maximum a posteriori estimation based wind fragility analysis with application to existing linear or hysteretic shear frames
Wang, Vincent Z., and Ginger, John D. (2014) Maximum a posteriori estimation based wind fragility analysis with application to existing linear or hysteretic shear frames. Structural Engineering and Mechanics, 50 (5). pp. 653-664.
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Abstract
Wind fragility analysis provides a quantitative instrument for delineating the safety performance of civil structures under hazardous wind loading conditions such as cyclones and tornados. It has attracted and would be expected to continue to attract intensive research spotlight particularly in the nowadays worldwide context of adapting to the changing climate. One of the challenges encumbering efficacious assessment of the safety performance of existing civil structures is the possible incompleteness of the structural appraisal data. Addressing the issue of the data missingness, the study presented in this paper forms a first attempt to investigate the feasibility of using the expectation-maximization (EM) algorithm and Bayesian techniques to predict the wind fragilities of existing civil structures. Numerical examples of typical linear or hysteretic shear frames are introduced with the wind loads derived from a widely used power spectral density function. Specifically, the application of the maximum a posteriori estimates of the distribution parameters for the story stiffness is examined, and a surrogate model is developed and applied to facilitate the nonlinear response computation when studying the fragilities of the hysteretic shear frame involved.
Item ID: | 34538 |
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Item Type: | Article (Research - C1) |
ISSN: | 1598-6217 |
Keywords: | wind fragility analysis; hysteresis; missing data; EM algorithm; Bayesian statistics; maximum a posteriori estimation; surrogate model |
Date Deposited: | 14 Aug 2014 03:03 |
FoR Codes: | 09 ENGINEERING > 0905 Civil Engineering > 090506 Structural Engineering @ 100% |
SEO Codes: | 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 100% |
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