Efficient Approach to System-Level Reliability-Based Design Optimization of Large-Scale Uncertain and Dynamic Wind-Excited Systems
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4, Issue 2
Abstract
This paper is focused on the development of an efficient system-level reliability-based design optimization strategy for uncertain wind-excited building systems characterized by high-dimensional design variable vectors (in the order of hundreds). Indeed, although a number of methods have been proposed over the last 15 years for the system-level reliability-based design optimization of building systems subject to stochastic excitation, few have treated problems characterized by more than a handful of design variables. This limits their applicability to practical problems of interest, such as the design optimization of high-rise buildings. To overcome this limitation, a simulation-based method is proposed in this work that is capable of solving reliability-based design optimization problems characterized by high-dimensional design variable vectors while considering system-level performance constraints. The framework is based on approximately decoupling the reliability analysis from the optimization loop through the definition of a system-level subproblem that can be fully defined from the results of a single simulation carried out in the current design point. To demonstrate the efficiency, practicality, and strong convergence properties of the proposed framework, a 40-story uncertain planar frame defined by 200 design variables is optimized under stochastic wind excitation.
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Acknowledgments
Support for this work provided by the National Science Foundation (Grant No. CMMI-1462084) is gratefully acknowledged.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 2 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jun 2, 2017
Accepted: Oct 26, 2017
Published online: Mar 9, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 9, 2018
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