Relationship between Pavement Roughness and Distress Parameters for Indian Highways
Publication: Journal of Transportation Engineering
Volume 139, Issue 5
Abstract
The present study demonstrates the relationships between pavement roughness and distress parameters like potholes, raveling, rut depth, cracked areas, and patch work. The pavement distress data collected on four national highways in India using a network survey vehicle (NSV) are used to develop linear and nonlinear regression models between roughness and distress parameters. Analysis of variance of these models indicated that nonlinear relation is better than a linear model. value, root mean square error (RMSE), and mean absolute relative error (MARE) also supported nonlinear models. An artificial neural network (ANN), which is an advanced technique of modeling, is also used in the present study to model pavement roughness with distress parameters. A network with five input nodes, 15 hidden nodes, and one output node is considered. The network was trained with 90% of the data and tested with remaining 10% data. Results of and MSE showed that the neural network performed highly significantly in both training and testing phases. Finally, the performance of the ANN model is compared with that of linear and nonlinear regression models. The mean absolute error (MAE) for the ANN model is around 18% less than that for the linear model and 11% less than that for the nonlinear model. MARE values are also 12.5% lower in the case of ANN modeling, indicating that the ANN model yields a better forecast of road roughness for a given set of distress parameters.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aguiar-Moya, J. P., Prozzi, J. A., and de Fortier Smit, A. (2011). “Mechanistic-empirical IRI model accounting for potential bias.” J. Transp. Eng., 137(5), 297–304.
Al-Mansour, A., and Alawal, R. (2006). “Correlation of visual inspection and roughness measurement in pavement condition evaluation.”, College of Engineering, Research Centre, King Saud Univ., Riyadh, Saudi Arabia.
Al-Omari, B., and Darter, M. I. (1995). “Effect of pavement deterioration types on IRI and rehabilitation.”, National Research Council, Washington, DC, 57–65.
Ceylan, H. (2002). “Analysis and design of concrete pavement systems using artificial neural networks.” Ph.D. dissertation, Univ. of Illinois at Urbana-Champaign, Urbana, IL.
Choi, J.-H., Adams, T. M., and Bahia, H. U. (2004). “Pavement roughness modeling using back-propagation neural networks.” Comput. Aided Civ. Infrastruct. Eng., 19(4), 295–303.
CSIR-Central Road Research Institute (CRRI). (2008). “Report on primary data collection through network survey vehicle (NSV).”, New Delhi, India.
Dayhoff, E. J. (1990). Neural network architecture: An introduction, Van Nostrand Reinhold, New York.
Garson, G. D. (1991). “Interpreting neural network connection weights, artificial intelligence.” AI Expert, 6(4), 47–51.
Gupta, A., Kumar, P., and Rastogi, R. (2011). “Pavement deterioration and maintenance model for low volume roads.” Int. J. Pavement Res. Technol., 4(4), 195–202.
Hawkeye Processing Toolkit Version 2.0 (2006). [Computer software]. ARRB Group Ltd, Melbourne, Australia.
Hozayen, A., and Alrukaibi, F. (2009). “Development of acceptance measures for long term performance of BOT highway projects.” Efficient Transport and Paving Systems, I. L. Al-Qadi, T. Sayed, N. A. Alnuaimi, and E. Masad, eds., CRC Press, London, 335–348.
Lin, J.-D., Yau, J.-T., and Hsiao, L.-H. (2003). “Correlation analysis between international roughness index (IRI) and pavement distress by neural network.” 82nd Annual Meeting (CD-ROM), Transportation Research Board, National Research Council, Washington, DC.
Liu, C., and Herman, R. (1996). “New approach to roadway performance indices.” J. Transp. Eng., 122(5), 329–336.
Mactutis, J. A., Sirous, H. A., and Weston, C. O. (2000). “Investigation of relationship between roughness and pavement surface distress based on WesTrack project.”, National Research Council, Washington, DC, 107–113.
MATLAB (2007). [Computer software]. Mathworks Inc, Natick, MA.
Mubaraki, M. (2009). “Predicting pavement condition deterioration for Saudi urban road network.” Geotechnical Special Publication No. 193, ASCE, Reston, VA, 56–61.
Roberts, C. A., and Attoh-Okine, N. O. (1998). “Comparative analysis of two artificial neural networks using pavement performance prediction.” Comput. Aided Civ. Infrastruct. Eng., 13(5), 339–343.
Rumelhart, H., Hinton, G. E., and Williams, R. J. (1986). Learning international representation by error propagation, in parallel distributed processing, MIT Press, Cambridge, MA.
Thube, D. T. (2012). “Artificial neural network (ANN) based pavement deterioration models for low volume roads in India.” Int. J. Pavement Res. Technol., 5(2), 115–120.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 139 • Issue 5 • May 2013
Pages: 467 - 475
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Dec 21, 2011
Accepted: Oct 25, 2012
Published online: Oct 29, 2012
Published in print: May 1, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.