Tatari, O., Sargand, S., Masada, T., and Tarawneh, B. (2013). ”Neural Network Approach to Condition Assessment of
Highway Culverts: Case Study in Ohio.” J. Infrastruct. Syst., 19(4), 409–414.
Technical Papers
Neural Network Approach to Condition Assessment of Highway
Culverts: Case Study in Ohio
Article History
Submitted: 31 December 2009
Accepted: 28 November 2012
Published: 15 November 2013
Publication Data
ISSN (print): 1076-0342
ISSN (online): 1943-555X
Publisher: American Society of Civil Engineers
Omer Tatari1; Shad M. Sargand2; Teruhisa Masada3; and Bashar Tarawneh4
1Alex
Alexander Faculty Fellow and Assistant Professor, Dept. of Civil, Environmental, and Construction Engineering, Univ. of Central
Florida, Orlando, FL 32816 (corresponding author). E-mail: tatari@ucf.edu
2Russ
Professor, Civil Engineering Dept., Ohio Univ., Athens, OH 45701.
3Professor,
4Assistant
Civil Engineering Dept., Ohio Univ., Athens, OH 45701.
Professor, Civil Engineering Dept., Univ. of Jordan, Amman 11942, Jordan.
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.
Permalink: http://dx.doi.org/10.1061/(ASCE)IS.1943-555X.0000139
ASCE Subject Headings: Culverts, Risk management, Neural networks, Inspection, Highways and roads, Case
studies, Ohio
Author keywords: Culverts, Risk assessment, Neural networks, Inspections, Highways
© 2013 American Society of Civil Engineers