Application of Soft Computing for Prediction of Pavement Condition Index
Publication: Journal of Transportation Engineering
Volume 138, Issue 12
Abstract
The pavement condition index (PCI) is a widely used numerical index for the evaluation of the structural integrity and operational condition of pavements. Estimation of the PCI is based on the results of a visual inspection in which the type, severity, and quantity of distresses are identified. The purpose of this study is to develop an alternative approach for forecasting the PCI using optimization techniques, including artificial neural networks (ANN) and genetic programming (GP). The proposed soft computing method can reliably estimate the PCI and can be used in a pavement management system (PMS) using simple and accessible spreadsheet softwares. A database composed of the PCI results of more than 1,250 km of highways in Iran was used to develop the models. The results showed that the ANN- and GP-based projected values are in good agreement with the field-measured data. In addition, the ANN-based model was more precise than the GP-based model. For more straightforward applications, a computer program was developed based on the results obtained.
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Acknowledgments
The writers would like to express their gratitude to RAHBORD Consulting Engineers Company and especially to Mr. Norouzi for their support during this research.
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Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 12 • December 2012
Pages: 1495 - 1506
Copyright
© 2012 American Society of Civil Engineers.
History
Received: May 7, 2011
Accepted: May 16, 2012
Published online: May 21, 2012
Published in print: Dec 1, 2012
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