A Fuzzy Expert System for Policy Making on Roads Pavement Maintenance

Document Type : Research Paper


Department of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran


Roads are one of the fundamental infrastructures for nations’ development, and their maintenance is of utmost importance. However, roads are subject to gradual deterioration due to vehicles’ continual run, climate change, and miscellaneous damages. Hence, road management centers in various countries, design and analyze a range of maintenance policies depending on road conditions through continuous monitoring. While deciding the time and type of road maintenance, has been traditionally done selectively by experts, regarding the large number of fragments in road networks, repeated and non-algorithmic nature of the decision making process, as well as the need for high precision to avoid over-budgeting, this task should be performed preferably by means of decision support systems. In order to determine appropriate actions for maintaining road fragments, pavement assessment indices must be measured at first, and then the right policy for maintaining each fragment or the whole road network must be planned based on the estimated maintenance costs and the allocated budget. In this paper, a fuzzy expert system is developed as a decision support system to assist road maintenance managers in their decision process by enhancing the speed and precision of policy making.


Main Subjects

1.   BBC, (2014). “Estimated road repairs cost rises to £12bn, survey says”, available at: http://www.bbc.com/news/uk-26862992
2. Chakrabarti, S., Kodikara, J. K. and Pardo, L. (2002). “Survey results on stabilisation methods and performance of local government roads in Australia”, Road Transp. Res., Vol. 11, No. 3, P. 3.
3. Kulkarni, R., Finn, F., Alviti, E., Chuang, J. and Rubinstein, J. (1983). “Development of a pavement management system”, Technical report, Kansas Department of Transportation (1983-89), 122 p.
4. Shahin, M. Y. (2005). Pavement management for airports, roads, and parking lots, Vol. 501. Springer, New York.
5. Finn, F. N., Terrel, R. L., Le, C. R. and Garrison, W. A. (1976). “Development of pavement management systems for programming roadway maintenance”, In Chemical Abstracts, Vol. 45, p. 286-330
6. Wang, Kelvin, Qiang Li, Kevin Hall, and Robert Elliott. "Experimentation with gray theory for pavement smoothness prediction”. Transportation Research Record: Journal of the Transportation Research Board 1990 (2007): pp. 3-13.
7. Janoff, M. S., Nick, J. B., Davit, P. S. and Hayhoe, G. F. (1985). “Pavement roughness and rideability”, NCHRP Rep., No. 275.
8. Magazine, C. I. (1980). “Pavement condition index (PCI)”, Concr. Int., Vol. 2, No. 09.
9. Kohn, Starr D., and Mohamed Y. Shahin. (1984) “Evaluation of the Pavement Condition Index for Use on Porous Friction Surfaces”. No. CERL-TR-M-351. Construction engineering research lab (army), Champaign IL, 1984.
10. VicRoads (2009), “Guide to Surface Inspection Rating”, Technical Bulletin 50, State Government of Victoria Australia, Sept. 2009.
11. Schnebele, E., Tanyu, B. F., Cervone, G. and Waters, N. (2015). “Review of remote sensing methodologies for pavement management and assessment”, Eur. Transp. Res. Rev., Vol. 7, No. 2, PP. 1–19.
12. U.S. Department of Transportation, “Roadway Distresses (AC),” U.S. Department of Transportation. [Online]. Available: https://faapaveair.faa.gov/Help/default.htm?turl=Documents%2Froadwaydistressesac.htm. [Accessed: 07-Feb-2017].
13. Texas Department of Transportation, “Principal Faults or Defects in Seal Coats or Surface Treatments,” Texas Department of Transportation. [Online]. Available: http://onlinemanuals.txdot.gov/txdotmanuals/ scm/principal_faults_or_defects_in_seal_coats_or_surface_treatments.htm. [Accessed: 07-Feb-2017].
14. Shahin, M. Y. and Walther, J. A. (1990). “Pavement maintenance management for roads and streets using the PAVER system”, Technical Report M-90/05, US Army Construction Engineering Research Laboratory, July 1990.
15. Nunez M. M. and Shahin, M. Y. (1986). "Pavement condition data analysis and modeling", Transportation Research Record, Vol. 1070, pp. 125-132.
16. Labi, S. and Sinha, K. C. (2005). “Life-cycle evaluation of flexible pavement preventive maintenance”, J. Transp. Eng., Oct. 2005, pp. 744-751.
17. Foo, H. C. and Akhras, G. (1995). “Prototype knowledge-based system for corrective maintenance of pavements”, J. Transp. Eng., Vol. 121, No. 6, PP. 517–523.
18. Feng, C. M. and Wang, T. C. (2003). “Highway emergency rehabilitation scheduling in post-earthquake 72 hours”, J. 5th East. Asia Soc. Transp. Stud., Vol. 5, PP. 3276–3285.
19. Johanns, M. and Craig, J. (2002) “Pavement Maintenance Manual.” Nebraska Department of Roads, USA.
20. Siler, W. and Buckley, J. J. (2005). Fuzzy expert systems and fuzzy reasoning, John Wiley & Sons.
21. Santana, M. (1995). “Managerial learning: A neglected dimension in decision support systems”, In System Sciences. Proceedings of the Twenty-Eighth Hawaii International Conference on, 1995, Vol. 4, PP. 82–91.
22. Yam, R. C. M., Tse, P. W., Li, L. and Tu, P. (2001). “Intelligent predictive decision support system for condition-based maintenance”, Int. J. Adv. Manuf. Technol., Vol. 17, No. 5, PP. 383–391.
23. Haas, R., Hudson, W. R. and Zaniewski, J. P. (1994). Modern pavement management. Krieger Pub Co., ISBN: 978-0894645884.
24. Ritchie, S. G., Yeh, C.-I., Mahoney, J. P. and Jackson, N. C. (1987). “Surface condition expert system for pavement rehabilitation planning”, J. Transp. Eng., Vol. 113, No. 2, PP. 155–167.
25. Sun L. and Gu, W. (2010). “Pavement condition assessment using fuzzy logic theory and analytic hierarchy process”, J. Transp. Eng., Vol. 137, No. 9, PP. 648–655.
26. Farhan, J. and Fwa, T. (2009). “Pavement maintenance prioritization using analytic hierarchy process”, Transp. Res. Rec. J. Transp. Res. Board, No. 2093, PP. 12–24.
27. Heravi, G. and Esmaeeli, A. N. (2013). “Fuzzy multicriteria decision-making approach for pavement project evaluation using life-cycle cost/performance analysis”, J. Infrastruct. Syst., Vol. 20, No. 2, pp. 04014002-1-7.
28. Liu, Y. and Sun, M. (2007). “Fuzzy optimization BP neural network model for pavement performance assessment”, In Grey Systems and Intelligent Services. GSIS 2007. IEEE International Conference on, 2007, PP. 1031–1034.
29. Bianchini, A. (2012). “Fuzzy representation of pavement condition for efficient pavement management”, Comput. Civ. Infrastruct. Eng., Vol. 27, No. 8, PP. 608–619.
30. Linstone, H. A. and Turoff, M. (1975). The Delphi method: Techniques and applications, Vol. 29. Addison-Wesley Reading, MA.