Abstract
Accurately determining the current state of the main cables of a suspension bridge is critical to assessing the safety of the bridge. These cables are composed of thousands of individual bridge wires, each of which deteriorates over time at a different rate. Current inspection methods provide an incomplete picture of the condition across the cable section, so cable strength estimation methods that rely on this inspection data involve considerable uncertainty. Furthermore, there is no method for estimating the continuing decline in cable strength following an inspection due to ongoing corrosion. This paper lays the groundwork for a time-dependent corrosion-rate model for bridge wires by using monitored environmental parameters from the cable interior. To establish this model, a methodology to estimate the annual corrosion rate as a function of environmental variables was proposed. First, experimental data on the corrosion rate of carbon steel from previous studies were analyzed using machine learning methods. Temperature, relative humidity, pH, and Cl− concentration were determined to be the most relevant variables for predicting the corrosion rate. Next, cyclic corrosion tests were performed by subjecting bridge wires to various levels of these environmental variables, and the resulting data were used to augment the experimental data set from previous studies. Finally, a corrosion-rate model that predicts the annual corrosion rate of bridge wires was developed using the augmented data set.
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Acknowledgments
This study was sponsored by the Federal Highway Administration under Contract DTFH61-04-C-00040 (program managers Dr. H. Ghasemi and Dr. P. Virmani). The support and guidance of Dr. Ghasemi and Dr. Virmani are greatly appreciated. The continuous suggestions by Dr. Bojidar Yanev, New York City Department of Transportation, were also greatly appreciated.
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Published In
Journal of Bridge Engineering
Volume 23 • Issue 6 • June 2018
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Jun 7, 2017
Accepted: Nov 22, 2017
Published online: Mar 16, 2018
Published in print: Jun 1, 2018
Discussion open until: Aug 16, 2018
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