Efficiently Implementing Genetic Optimization with Nonlinear Response History Analysis of Taller Buildings
Publication: Journal of Structural Engineering
Volume 140, Issue 8
Abstract
Nonlinear response history analysis is an important tool for accurately determining the performance of tall buildings under severe earthquake loading. When a standard genetic algorithm is used in conjunction with nonlinear response history analysis, it is desirable to use smaller generation sizes because of the computational effort to analyze individual designs. A study was conducted to evaluate how different genetic algorithm techniques influence the reliability and efficiency of the algorithm when used with nonlinear response history analysis and small generation sizes. The system used in the study was a nine-story buckling restrained braced frame that was optimized to minimize brace areas under individual earthquake records. A baseline study showed that a typical genetic algorithm did not converge to the same best design for different random number sequences (seed numbers). Forced diversity improved the reliability of the algorithm such that it converged to the same optimum, regardless of initial seed number. Adaptive mutation decreased the required number of generations when coupled with a noncrossover constraint. Consecutive identical generations were found to predict convergence and provide a basis for an exit criterion.
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Acknowledgments
This work was possible due to support from Brigham Young University. Graham Oxborrow programmed the genetic algorithm into OpenSees as part of another project.
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Published In
Journal of Structural Engineering
Volume 140 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 21, 2012
Accepted: Sep 9, 2013
Published online: Mar 17, 2014
Published in print: Aug 1, 2014
Discussion open until: Aug 17, 2014
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