Title: Forecasting long-term bridge deterioration conditions using artificial intelligence techniques
Authors: Patrick A. Creary; Fang Clara Fang
Addresses: Department of Civil, Environmental and Biomedical Engineering, College of Engineering, Technology, and Architecture, University of Hartford, 200 Bloomfield Avenue, West Hartford, CT, 06117, USA ' Department of Civil, Environmental and Biomedical Engineering, College of Engineering, Technology, and Architecture, University of Hartford, 200 Bloomfield Avenue, West Hartford, CT, 06117, USA
Abstract: About a quarter of 600,000 bridges in the USA are deficient. The objective of this study is to make accurate predictions of future bridge deterioration condition using artificial neural networks, and to ensure the developed models are applicable for practical use. Using bridge inspection data provided by the Connecticut Department of Transportation (ConnDOT), artificial neural networks-based model is developed to modelling complex relationships between input and output to identify patterns within the data. The developed neural net input variables included bridge geometry, construction and service, while the output variables were condition ratings for the deck, superstructure, and substructure. The neural nets used in this research demonstrated an ability to produce accurate results down to a root mean square error of 10.05% on the best trial. This study shows the potential to develop a tool to predict the future condition ratings of bridges to assist agencies in bridge program planning.
Keywords: bridge management; bridge conditions; artificial neural networks; ANNs; time-series forecasting; long-term deterioration; bridge deterioration; artificial intelligence; modelling; bridge maintenance planning.
International Journal of Intelligent Systems Technologies and Applications, 2014 Vol.13 No.4, pp.280 - 293
Available online: 13 Apr 2015Full-text access for editors Access for subscribers Purchase this article Comment on this article