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Rutting Model for HMA Overlay Treatment of Composite Pavements

DOI: 10.1155/2013/176029

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Abstract:

Timely rehabilitation and preservation of pavement systems are imperative to maximize benefits in terms of driver’s comfort and safety. However, the effectiveness of any treatment largely depends on the time of treatment and triggers governed by treatment performance models. This paper presents the development of rutting model for overlay treatment of composite pavement in the State of Louisiana. Various factors affecting the rutting of overlay treatment were identified. Regression analysis was conducted, and rut prediction model is generated. In order to better predict the pavement service life, the existing condition of the pavement was also utilized through the model. The developed models provided a good agreement between the measured and predicted rut values. It was found that the predictions were significantly improved, when existing pavement condition was incorporated. The resulting rutting model could be used as a good pavement management tool for timely pavement maintenance and rehabilitation actions to maximize LADOTD benefits and driver’s comfort and safety. 1. Introduction and Background Rutting is considered as one of the major forms of distresses in HMA overlay of composite pavement. Rutting is a surface depression in the wheel paths generally caused by truck tire pressures, axle loads, and traffic volumes [1]. Longitudinal deviation of rut depth in the wheel path is a primary factor in the road roughness which affects serviceability and IRI (International Roughness Index) [2]. Pavement roughness influences pavement ride quality and usually leads to rider discomfort, increased travel times, and higher operational cost for vehicle. In the transverse direction of pavement, rutting along the wheel path hampers drainage characteristics, reduces runoff capability, and causes hydroplaning and loss of friction [3, 4]. Longitudinal crack, which often occurs in deep ruts, induces the penetration of water and other debris, accelerates the rate of deterioration of HMA overlay and underlying PCC layer, and reduces the pavement service life [3]. Regarding HMA overlay rutting, it is commonly believed that rutting is a demonstration of two different mechanisms and is a combination of densification (change in volume) and repetitive shear deformation (lateral movement or plastic flow with no change in volume) [5]. Both densification and shear deformation are strongly influenced by traffic loading, pavement structure, and pavement material properties. Climate shows significant effect on rutting development, when the subgrade experiences seasonal variations

References

[1]  T. D. White, J. E. Haddock, A. J. T. Hand, and H. FANG, “Contributions of pavement structural layers to rutting of hot mix asphalt pavements,” NCHRP Report 468, Transportation Research Board, Washington, DC, USA, 2002.
[2]  ARA Inc, ERES Division, “Appendix gg-1: calibration of permanent deformation models for flexible pavements,” National Cooperative Highway Research Program. Transportation Research Board, Washington, DC, USA, 2004.
[3]  E. Oscarsson, “Evaluation of the Mechanistic-empirical pavement design guide model for permanent deformations in asphalt concrete,” International Journal of Pavement Engineering, vol. 12, no. 1, pp. 1–12, 2011.
[4]  R. Luo and J. A. Prozzi, “Development of a pavement rutting model from long-term pavement performance data,” S.l. : TRB Committee AFS50, 2008.
[5]  S. Hu, F. Zhou, and T. Scullion, “Development, calibration, and validation of a new M-E rutting model for HMA overlay design and analysis,” Journal of Materials in Civil Engineering, vol. 23, no. 2, pp. 89–99, 2011.
[6]  ARA Inc., ERES Division, “Design of new and reconstructed rigid pavements,” in Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures, Chapter 4, National Cooperative Highway Research Program, Champaign, Ill, USA, 2001.
[7]  K. P. George, “MDOT pavement management system: prediction models and feedback system,” Tech. Rep. 39215-1850, Mississippi Department of Transportation, Jackson, Miss, USA, 2000.
[8]  N. Jackson and J. Puccinelli, “Long-Term Pavement Performance (LTPP) data analysis support: national pooled fund study Tpf-5(013),” Tech. Rep. FHWA-HRT-06-121, Office of Infrastructure R&D, Federal Highway Administration, 2006.
[9]  J. R. Chang, D. H. Chen, and C. T. Hung, “Selecting preventive maintenance treatments in Texas: using the technique for order preference by similarity to the ideal solution for specific pavement study-3 sites,” Transportation Research Record, no. 1933, pp. 62–71, 2005.
[10]  M. J. Khattak and G. Y. Baladi, “Review of the LADOTD pavement treatment practices and project selection for development of treatment performance models,” Tech. Rep. 70804-9245, Louisiana Department of Transportation and Development, Baton Rouge, La, USA, 2011.
[11]  M. Azpurua and K. dos Ramos, “A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude,” Progress In Electromagnetics Research M, vol. 14, pp. 135–145, 2010.
[12]  G. Zhou, L. Wang, and Y. Lu, “IRI model enhancement for flexible pavement design using LTPP data,” Transportation Research Board, Washington, DC, USA, 2008.
[13]  Y. H. Huang, Pavement Analysis and Design, Pearson Prentice, Upper Saddle River, NJ, USA, 2nd edition, 2004.

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