Structural Time-Dependent Reliability Assessment with New Power Spectral Density Function
Publication: Journal of Structural Engineering
Volume 145, Issue 12
Abstract
An important element of time-dependent reliability analysis of civil structures is choosing a proper model for the applied loads. Stochastic process theory has been widely used in existing studies to perform structural time-dependent reliability analysis. However, the use of many types of power spectral density functions leads to an inefficient calculation of structural reliability. This paper proposes an analytical method for structural reliability assessment, where a new power spectral density function is developed to enable the reliability analysis to be conducted with a simple and efficient formula. A non-Gaussian load process, if present, is first converted into an equivalent Gaussian process to improve the assessment accuracy. Illustrative examples are presented to demonstrate the applicability of the proposed method. Results show that a greater autocorrelation in the load process leads to a smaller failure probability. The structural reliability may be significantly overestimated if one simply treats the non-Gaussian load process as a Gaussian one. Moreover, the impact of modeling the load process as a continuous process or a discrete one on structural reliability is also investigated.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The research described in this paper was supported by the Faculty of Engineering and IT Ph.D. Research Scholarship (SC1911) from the University of Sydney. This support is gratefully acknowledged. The authors would like to acknowledge the thoughtful suggestions of two anonymous reviewers, which substantially improved the present paper.
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Published In
Journal of Structural Engineering
Volume 145 • Issue 12 • December 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Feb 22, 2018
Accepted: May 15, 2019
Published online: Oct 10, 2019
Published in print: Dec 1, 2019
Discussion open until: Mar 10, 2020
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