Performance-Based Wind-Resistant Optimization Design for Tall Building Structures
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Volume 145, Issue 10
Abstract
This paper presents a novel framework for the performance-based wind-resistant design of tall buildings accounting for the joint probability distribution of wind speed and direction obtained from long-term wind measurements of local meteorological stations. The joint probability distribution of the wind speed and direction, as well as the uncertainties and probability distributions of natural frequencies and damping ratios, are integrated to investigate the realistic structural performance of optimized target buildings under the wind action. A procedure on the performance-based wind-resistant optimization design of tall buildings is then proposed by adopting the optimality criteria algorithm. The proposed framework is applied to the Commonwealth Advisory Aeronautical Research Council building; the total weight and sizes of structural members are selected as the objective function and design variables in the optimization process, respectively. Compared with the deterministic constraints adopted in the conventional optimization procedure, the optimization constraint conditions in this paper are expressed by the failure probabilities in terms of the maximum displacement, interstory drift, and wind-induced acceleration responses at different design levels, which are associated with different return periods required by the strength and serviceability criteria in the performance-based wind engineering design. The mapping transformation method and checking point algorithm in structural reliability theory are adopted to conduct reliability analysis on the probability constraints in the optimization procedure. The optimized results are analyzed and discussed in the numerical example to verify the effectiveness of the proposed performance-based wind-resistant optimization design of tall buildings.
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Acknowledgments
The authors would like to gratefully acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 51578169, 51778161, and 51808153), the Innovation Project of Educational Department of Guangdong Province (Grant No. 2016KQNCX117), and Technology Planning Project of Guangzhou City (Grant No. 201804010267).
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Published In
Journal of Structural Engineering
Volume 145 • Issue 10 • October 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jan 15, 2018
Accepted: Feb 11, 2019
Published online: Aug 5, 2019
Published in print: Oct 1, 2019
Discussion open until: Jan 5, 2020
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