Stay Cable Tension Estimation of Cable-Stayed Bridges Using Genetic Algorithm and Particle Swarm Optimization
Publication: Journal of Bridge Engineering
Volume 22, Issue 10
Abstract
This study presents a methodology for estimating stay cable tensions of cable-stayed bridges using genetic algorithm (GA) and particle swarm optimization (PSO). At first, a comprehensive cable model was used to represent the dynamic behavior of the cables. Second, an error function corresponding to the difference between the experimentally measured natural frequencies of the cable and the analytical natural frequencies (obtained using the preferred cable model) was introduced. GA and PSO were then used for minimizing the error function to estimate the cable tension. Because of the stochastic nature of evolutionary algorithms (EAs), results of the minimization problem were reported statistically over multiple runs. Following the proposed methodology, bending stiffness of the cable, sag extensibility, and the effect of the cable crossties (that are used to suppress the wind- and rain-induced vibration of cables) were considered. The accuracy of the proposed method was evaluated using simulation results of a tensioned cable and experimental results of a cable-stayed bridge.
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Acknowledgments
This research is supported by the Ohio Department of Transportation (ODOT). In addition, the authors are grateful for the lift-off tensions measured and provided by VSL company.
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Published In
Journal of Bridge Engineering
Volume 22 • Issue 10 • October 2017
Copyright
© 2017 American Society of Civil Engineers.
History
Received: Dec 1, 2016
Accepted: May 16, 2017
Published online: Aug 11, 2017
Published in print: Oct 1, 2017
Discussion open until: Jan 11, 2018
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