Title

Neural Network Approach to Condition Assessment of Highway Culverts: Case Study in Ohio

Authors

Authors

O. Tatari; S. M. Sargand; T. Masada;B. Tarawneh

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

J. Infrastruct. Syst.

Keywords

Culverts; Risk assessment; Neural networks; Inspections; Highways; RISK-ASSESSMENT; PIPE; INSPECTION; MANAGEMENT; Engineering, Civil

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 5years, 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. (C) 2013 American Society of Civil Engineers.

Journal Title

Journal of Infrastructure Systems

Volume

19

Issue/Number

4

Publication Date

1-1-2013

Document Type

Article

Language

English

First Page

409

Last Page

414

WOS Identifier

WOS:000329913100006

ISSN

1076-0342

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