Development of a Long-Term Bridge Element Performance Model Using Elman Neural Networks
Publication: Journal of Infrastructure Systems
Volume 20, Issue 3
Abstract
A reliable deterioration model is essential in bridge asset management. Most deterioration modeling requires a large amount of well-distributed condition rating data along with all bridge ages to calculate the probability of condition rating deterioration. This means that the model can only function properly when a full set of data is available. To overcome this shortcoming, an improved artificial intelligence (AI)-based model is presented in this study to effectively predict long-term deterioration of bridge elements. The model has four major components: (1) categorizing bridge element condition ratings; (2) using the neural network-based backward prediction model (BPM) to generate unavailable historical condition ratings for applicable bridge elements; (3) training by an Elman neural network (ENN) for identifying historical deterioration patterns; and (4) using the ENN to predict long-term performance. The model has been tested using bridge inspection records that demonstrate satisfactory results. This study primarily focuses on the establishment of a new methodology to address the research problems identified. A series of case studies, hence, need to follow to ensure the method is appropriately developed and validated.
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Acknowledgments
The authors acknowledge the financial support provided by the Australian Research Council through an ARC Linkage Project (LP0883807). The authors also wish to thank the industry partners, Queensland Department of Transport and Main Roads and Gold Coast City Council, for their financial and in-kind support.
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Published In
Journal of Infrastructure Systems
Volume 20 • Issue 3 • September 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jan 30, 2012
Accepted: Oct 31, 2013
Published online: Feb 20, 2014
Discussion open until: Jul 20, 2014
Published in print: Sep 1, 2014
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