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First published online January 1, 2010

Pavement Performance Modeling Using Piecewise Approximation

Abstract

A successful pavement management system requires an accurate pavement performance prediction model. A novel pavement performance model using the piecewise approximation approach was developed to estimate the pavement serviceable life. It can be broadly applied to estimate pavement performance of any distress types or indexes. The basic theory of the piecewise approximation is to divide the whole pavement serviceable life into three zones: Zone 1 for early age pavement distress, Zone 2 in rehabilitation stage, and Zone 3 for overdistressed situations. Historical pavement performance data are regressed independently in each time zone. This approach can accurately predict pavement distress progression trends in each individual zone by eliminating possible impacts from biased data in other zones. This paper describes the theoretical piecewise approximation process of data classification and model regression and then demonstrates an implementation for a group of Washington State Department of Transportation asphalt concrete pavements. The results are compared with the Mechanistic–Empirical Pavement Design Guide incremental damage approach, the current Washington State Pavement Management System (WSPMS) exponential model, and ordinary regression on all data points. Results indicate that the proposed approach is able to estimate the most accurate rehabilitation due year and to predict the performance trends for each divided zone. The piecewise approximation approach is planned for implementation into the WSPMS and will play an important role in decision making for future pavement rehabilitations.

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References

1. Lytton R. L. Concepts of Pavement Performance Prediction and Modeling. Proc., 2nd North American Conference on Managing Pavements, Vol. 2, Toronto, Canada, 1987.
2. AASHTO Guide for Design of Pavement Structures. AASHTO, Washington, D.C., 1993.
3. ARA, Inc., ERES Consultants Division. Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures. Final report, NCHRP Project 1–37A. Transportation Research Board of the National Academies, Washington, D.C., 2004. http://www.trb.org/mepdg/guide.htm.
4. Kajner L., Kurlanda M., and Sparks G. A. Development of Bayesian Regression Model to Predict Hot-Mix Asphalt Concrete Overlay Roughness. In Transportation Research Record 1539, TRB, National Research Council, Washington, D.C., 1996, pp. 125–131.
5. Butt A. A., Shahin M. Y., Feighan K. J., and Carpenter S. H. Pavement Performance Prediction Model Using the Markov Process. In Transportation Research Record 1123, TRB, National Research Council, Washington, D.C., 1987, pp. 12–19.
6. Prozzi J. A., and Madanat S. M. Development of Pavement Performance Models by Combining Experimental and Field Data. Journal of Infrastructure Systems, Vol. 10, Issue 1, March 2004, pp. 9–22.
7. Hong F., and Prozzi J. A. Estimation of Pavement Performance Deterioration Using Bayesian Approach. Journal of Infrastructure Systems, Vol. 12, Issue 1, June 2006, pp. 77–86.
8. Washington State Pavement Management System (WSPMS), Version 2008. Materials Laboratory, Washington State Department of Transportation, Tumwater, Wash., 2009.
9. Kay R. K., Mahoney J. P., and Jackson N. C. The WSDOT Pavement Management System: 1993 Update. Washington State Transportation Center, Washington State Department of Transportation, Olympia, WA, 1993.
10. Greene W. H. Econometric Analysis, 5th ed. New York University Leonard N. Stern School of Business, New York, 2003.
11. Muench S. T., White G. C., Mahoney J. P., Turkiyyah G. M., Sivaneswaran N., Pierce L. M., and Li J. Washington State's Web-Enabled Pavement Management System. In Transportation Research Record: Journal of the Transportation Research Board, No. 1889, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 35–46.
12. Econometric Software, Inc. NLOGIT 3.0. Plainview, N.Y., 2007.
13. Li J., Pierce L. M., and Uhlmeyer J. Calibration of the Flexible Pavement Portion of the Mechanistic–Empirical Pavement Design Guide for the Washington State Department of Transportation. In Transportation Research Record: Journal of the Transportation Research Board, No. 2095, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 73–83.
14. Li J., Muench S. T., Mahoney J. P., Sivaneswaran N., and Pierce L. M. Calibration of NCHRP 1–37A Software for the Washington State Department of Transportation: Rigid Pavement Portion. In Transportation Research Record: Journal of the Transportation Research Board, No. 1949, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 43–53.
15. Li J., Uhlmeyer J. S., Mahoney J. P., and Muench S. T. Use of AASHTO 1993 Guide, MEPDG, and Historical Performance to Update WSDOT Pavement Design Catalog. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
16. Guillaumot V. M., Durango-Cohen P. L., and Madanat S. M. Adaptive Optimization of Infrastructure Maintenance and Inspection Decisions Under Performance Model Uncertainty. Journal of Infrastructure Systems, Vol. 9, Issue 4, 2003, pp. 133–139.
17. Bezdek J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, Mass., 1981.

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Article first published online: January 1, 2010
Issue published: January 2010

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© 2010 National Academy of Sciences.
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Authors

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Jianhua Li
Washington State Department of Transportation, P.O. Box 47365, Olympia, WA 98504-7365.
David R. Luhr
Washington State Department of Transportation, P.O. Box 47365, Olympia, WA 98504-7365.
Jeff S. Uhlmeyer
Washington State Department of Transportation, P.O. Box 47365, Olympia, WA 98504-7365.

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