Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data

Document Type: Regular Paper


1 Department of Civil Engineering, Urmia University

2 Faculty of Civil Engineering, Florida International University, Miami, FL, USA


Deterioration models are the essential parts of any Pavement Management System (PMS). These models are employed to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are in consonance with roughness which is one of the most important indices in pavement evaluation. High correlation between International Roughness Index (IRI) and user comfort led to modeling pavement deterioration based on IRI during PMS history. On the other hand, in recent years Artificial Neural Network (ANN) which is a valuable tool of soft computing is used in pavement modeling, broadly. This study assessed the development of an ANN pavement deterioration model based on IRI applying Back-Propagation Neural Networks (BPNN) technique. The Long-Term Pavement Performance (LTPP) data was extracted from two General Pavement Study (GPS) sections including GPS-1 and GPS-2. After training and testing the developed model, results were compared with a polynomial regression model. Results revealed that predicted IRI values with developed ANN model have a good correlation with measured values rather than the polynomial regression model for both GPS-1 and GPS-2 sections.


Main Subjects

[1] Haas, R., Hudson, W. R. and Falls, L. C. (2015). Pavement Asset Management, Scrivener Publishing with John Wiley & Sons.
[2] Ozbay, K. and Laub, R. (2001). Models for Pavement Deterioration Using LTPP, Report no. FHWA-NJ-1999-030, Federal Highway Administration, Washington, D.C.
[3] Haas, R., Hudson, W. R. and Zaniewski, J. P. (1994). Modern pavement management, Krieger, Malabor, Fla.
[4] Kim, Y.R. (2009). Modeling of asphalt concrete, ASCE press, McGraw-Hill.
[5] Bekheet, W., Helali, K., Halim, A. and Springer, J. (2005). A Comprehensive Approach for the Development of Performance Models for Network-Level PMS Using LTPP Data, Proceedings of 84th Annual Meeting of TRB, Washington, D.C.
[6] Zhou, X. and Damnjanovic, I. D. (2011). Optimal Hedging of Commodity Price Risks in Highway Contracts, Proceedings of 90th Annual Meeting of TRB, Washington, D.C.
[7] Shahin, M. Y. (2005). Pavement Management for Airports, Roads, and Parking Lots, Chapman & Hall, N.Y.
[8] AASHTO. (1993). AASHTO Guide for Design of Pavement Structures, American Association of State Highway and Transportation Officials, Washington, D.C.
[9] Porras-Alvarado, J. D., Zhang, Z. and Salazar, L. G. L. (2014). Probabilistic Approach to Modeling Pavement Performance Using IRI Data, Proceedings of 93rd Annual Meeting of TRB, Washington, D.C.
[10] AASHTO. (2001). AASHTO Pavement Management Guide, American Association of State Highway and Transportation Officials, Washington, D.C.
[11] Tsunokawa, K. and Schofer, J. (1994). Trend Curve Optimal Control Model for Highway Pavement Maintenance: Case Study and Evaluation, Transportation Research, Part A, 28(2), 151–166.
[12] Smith, J. and Tighe, S. (2004). Assessment of Overlay Roughness in Long-Term Pavement Performance – Canadian Case Study, Proceedings of 83th Annual Meeting of TRB, Washington, D.C.
[13] Arifuzzaman, M. (2017). Advanced ANN prediction of moisture damage in cnt modified asphalt binder. Soft Computing in Civil Engineering, 1(1), 1-11. DOI: 10.22115/scce.2017.46317
[14] Rezazadeh Eidgahee, D., Haddad, A., & Naderpour, H. (2018). Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Scientia Iranica. DOI: 10.24200/SCI.2018.5663.1408
[15] Naderpour, H., Eidgahee, D. R., Fakharian, P., Rafiean, A. H., & Kalantari, S. M. (2019). A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Engineering Science and Technology, an International Journal. DOI: 0.1016/j.jestch.2019.05.013
[16] Rezazadeh Eidgahee, D., Rafiean, A. H., & Haddad, A. (2019). A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches. Iranian Journal of Science and Technology, Transactions of Civil Engineering, DOI: 10.1007/s40996-019-00263-1
[17] Naderpour, H., Nagai, K., Fakharian, P., & Haji, M. (2019). Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Composite Structures, 215, 69-84. DOI: 10.1016/j.compstruct.2019.02.048
[18] Kargah-Ostadi, N., Stoffels, S. and Tabatabaee, N. (2010). Network-Level Pavement Roughness Prediction Model for Rehabilitation Recommendations, Proceedings of 89th Annual Meeting of TRB, Washington, D.C.
[19] ASTM E1274–18. (2018). Standard Test Method for Measuring Pavement Roughness Using a Profilograph, ASTM International, West Conshohocken, PA.
[20] Mohamed Jaafar, Z. F., Uddin, W. and Najjar, Y. (2016). Asphalt Pavement Roughness Modeling Using the Artificial Neural Network and Linear Regression Approaches for LTPP Southern U.S. States, Proceedings of 95th Annual Meeting of TRB, Washington, D.C.
[21] Khattak, M. J., Nur, M. A., Bhuyan, M. R-U-K. and Gaspard, K. (2013). International Roughness Index Models for HMA Overlay Treatment of Flexible and Composite Pavements for Louisiana, Proceedings of 92nd Annual Meeting of TRB, Washington, D.C.
[22] Bekley, M. E. (2016). Pavement Deterioration Modeling Using Historical Roughness Data, M.Sc. Thesis, Arizona State University.
[23] Soncim, S. P. and Fernandes, J. L. (2013). Roughness Performance Model for Double Surface Treatment Highways, Proceedings of 92nd Annual Meeting of TRB, Washington, D.C.
[24] Smith, B. (2014). Factors Affecting the IRI of Asphalt Overlays, Proceedings of 93rd Annual Meeting of TRB, Washington, D.C.
[25] FHWA. (2009). Long-Term Pavement Performance Information Management System: Pavement Performance Database User Reference Guide, Publication No. FHWA-RD-03-088, Federal Highway Administration, Washington, D.C.
[26] Solatifar, N., Behnia, C. and Aflaki, S. (2011). A Review to Experiences of Different Countries in Implementing Long-Term Pavement Performance (LTPP) Program, Proceedings of 6th National Congress on Civil Engineering, Semnan, Iran.
[27] Nassiri, S., Shafiee, M. H. and Bayat, A. (2013). Development of Roughness Models Using Alberta Transportation’s Pavement Management System, Proceedings of 92nd Annual Meeting of TRB, Washington, D.C.
[28] FHWA. (2009). Long-Term Pavement Performance (LTPP) Standard Data Release 23.0., Federal Highway Administration, <> (May. 19, 2011).
[29] Lee, D., Derrible, S. and Pereira, F. C. (2018). Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling, Transportation Research Record: Journal of the Transportation Research Board, 2672(49), 101-112.
[30] Xu, L-N. (2003). Artificial Neural Network Control, Publishing House of Electronics Industry, Beijing, pp. 27-41.