Efficient and Comprehensive Time-Dependent Reliability Analysis of Complex Structures by a Parameter State Model
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 2
Abstract
A major challenge in a time-dependent reliability analysis is to find a good balance between computational effort, accuracy, and comprehension of the analysis. In this contribution, computational efforts are reduced, and comprehension is increased by changing the perspective from a classical time-dependent probabilistic model to an equivalent parameter state model. The model maps all time-dependent information to one stochastic variable, allowing one to analyze the original problem by analyzing changes in the distribution of this single variable. This clear-cut structure allows for analyzing and visualizing dependencies in detail as well as a novel perspective on reliability estimation, even in complex settings. Furthermore, it just needs computational effort as low as that for a static reliability estimation in many cases. Our approach utilizes, but is not restricted to, Subset Simulation and is therefore especially designed for complex engineering structures of high dimension. To illustrate the model and its computational efficiency, an illustrative example in the time-dependent reliability analysis of concrete structures is presented, and it is shown how to analyze complex networks potentially.
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Data Availability Statement
The code used during the study is available from the corresponding author on request.
Acknowledgments
We are very grateful for the support granted within the RTG GrK 1932 Stochastic Models for Innovations in Engineering Science, which is funded by the Deutsche Forschungsgemeinschaft (DFG).
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 2 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 10, 2020
Accepted: Dec 15, 2020
Published online: Mar 24, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 24, 2021
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