Abstract
This study proposes an alternative and competitive methodology for predicting solitary wave forces on coastal bridge decks using artificial neural networks (ANNs). It is imperative to accurately predict the on-deck wave forces for the design and retrofit of coastal bridges subject to the potential impact of hurricanes and tsunamis. For this purpose, ANNs are used to determine wave loads based on a valid data set. First, the structural, fluid, and wave variables involved in the bridge deck-wave interaction are briefly introduced. The back-propagation network (BPN) wave force prediction model trained using the back-propagation algorithm is highlighted. A data set with 472 evidence cases is prepared based on extensive computational fluid dynamics (CFD) simulations. Three major input variables, the still-water level (SWL), bottom elevation of the girder/superstructure, and wave height, are selected. Then, the procedures of training the ANNs for the vertical and horizontal forces are presented in detail. Finally, the trained network structures with high predictive skills after substantial training are given with a proposed predictive equation for the vertical and horizontal forces. The results showed that the ANN methodology is robust and capable of capturing the underlying physical complexity in the bridge deck-wave interaction.
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Acknowledgments
This work was supported by National Science Foundation (NSF) grants CCF-1539567 and ACI-1338051. High-performance computing resources were provided by Louisiana State University (LSU), and they were highly appreciated. All the findings presented in this study are those of the authors and do not necessarily represent those of the sponsors.
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Published In
Journal of Bridge Engineering
Volume 23 • Issue 5 • May 2018
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Jun 9, 2017
Accepted: Oct 6, 2017
Published online: Mar 12, 2018
Published in print: May 1, 2018
Discussion open until: Aug 12, 2018
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