Neural Network Approach to Condition Assessment of Highway Culverts: Case Study in Ohio
Publication: Journal of Infrastructure Systems
Volume 19, Issue 4
Abstract
Millions of culverts exist in the United States, and they are aging rapidly. Inspection of all the culverts consumes a lot of time and resources. Instead of inspecting each culvert every 5 years, this study presents a more intelligent approach to predict the condition of each culvert. An artificial neural network (ANN) model is built to assess the condition of the culverts based on culvert inventory data. The overall condition-rating predictions are compared with the condition rating based on manual inspection. The results of this study have shown that ANN was able to predict culvert adjusted overall rating with high precision, as the course of action score prediction rate was 100%. Sensitivity analysis of the ANN model is provided to assess the effect of variables. The goal of this study is to show that more intelligent culvert-management systems could be devised by taking advantage of artificial intelligence.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 19 • Issue 4 • December 2013
Pages: 409 - 414
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Dec 31, 2009
Accepted: Nov 28, 2012
Published online: Nov 15, 2013
Published in print: Dec 1, 2013
Discussion open until: Apr 15, 2014
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