Expected Performance of Pavement Repair Works in a Global Network Optimization Model
Publication: Journal of Infrastructure Systems
Volume 13, Issue 2
Abstract
A global network optimization model has been developed for generating a pavement repair plan using expected performance of pavement repair works. The expected performance of pavement repair works is represented by the expected age (service life) associated with potential repair actions. The expected age defines the anticipated pavement condition improvement obtained from applying a particular repair action. The expected age for each repair action is usually known from either experience or assumed as part of a design procedure. A constrained linear optimization model is formulated with its main objective of optimizing the expected pavement condition improvement. Pavement condition improvement is defined as the age gain in year lane kilometer or average age in years extended to a pavement network as a result of applying potential repair actions. The global linear model is subjected to a single budget constraint enforcing the total budget available for the entire network and limitation constraints placing lower and upper limits on the repair variables. The optimum repair plan provides a macroscopic solution as the repair variables represent pavement proportions that should be treated by the corresponding repair actions. The pavement network is divided into a number of systems with similar pavement structures and loading conditions. Presented sample results have indicated that global network optimization of the pavement management problem may not result in a rational budget allocation among deployed pavement systems. This problem can be solved by enforcing system improvement requirement constraints.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abaza, K. A., and Ashur, S. A. (1999). “An optimum decision policy for management of pavement maintenance and rehabilitation.” Transportation Research Record. 1655, Transportation Research Board, Washington, D.C., 8–15.
Abaza, K. A., Ashur, S. A., Abu-Eisheh, S., and Rabay’a, A. (2001). “Macroscopic optimum system for management of pavement rehabilitation.” J. Transp. Eng., 127(6), 493–500.
Abaza, K. A., Ashur, S. A., and Al-Khatib, I. (2004). “Integrated pavement management system with a Markovian prediction model.” J. Transp. Eng., 130(1), 24–33.
Bazaraa, M. S., and Shetty, C. M. (1979). Nonlinear programming: Theory and algorithms, Wiley, New York.
Butt, A. A., Shahin, M. Y., Feighan, K. J., and Carpenter, S. H. (1987). “Pavement performance prediction model using the Markov process.” Transportation Research Record. 1123, Transportation Research Board, Washington, D.C., 12–19.
Chen, X., Hudson, S., Pajoh, M., and Dickinson, W. (1996). “Development of new network optimization model for Oklahoma Department of Transportation.” Transportation Research Record. 1524, Transportation Research Board, Washington, D.C., 103–108.
Ferreira, A., Antunes, A., and Picado-Santos, L. (2002). “Probabilistic segment-linked pavement management optimization model.” J. Transp. Eng., 128(6), 568–577.
George, K. P., Rajagopal, A. S., and Lim, L. K. (1989). “Models for predicting pavement deterioration.” Transportation Research Record. 1215, Transportation Research Board, Washington, D.C., 1–7.
Grivas, D. A., Ravirala, V., and Schultz, B. C. (1993). “State increment optimization methodology for network-level pavement management.” Transportation Research Record. 1397, Transportation Research Board, Washington, D.C., 25–33.
Harper, W., and Majidzadeh, K. (1991). “Use of expert opinion in two pavement management systems.” Transportation Research Record. 1311, Transportation Research Board, Washington, D.C., 242–247.
Hill, L., Cheetham, A., and Hass, R. (1991). “Development and implementation of a pavement management system for Minnesota.” Transportation Research Record. 1311, Transportation Research Board, Washington, D.C., 230–241.
Liu, F., and Wang, K. C. P. (1996). “Pavement performance-oriented network optimization system.” Transportation Research Record. 1524, Transportation Research Board, Washington, D.C., 86–93.
Pedigo, R. D., Hudson, W. R., and Roberts, F. L. (1982). “Pavement performance modeling for pavement management.” Transportation Research Record. 814, Transportation Research Board, Washington, D.C., 14–21.
Phillips, D. T., Ravindran, A., and Solberg, J. J. (1976). Operations research: Principles and practice, Wiley, New York.
Pilson, C., Hudson, W. R., and Anderson, V. (1999). “Multi-objective optimization in pavement management using genetic algorithms and efficient surfaces.” Transportation Research Record. 1655, Transportation Research Board, Washington, D.C., 42–48.
Shahin, M. Y. (1994). Pavement management for airports, roads, and parking lots, Chapman and Hall, New York.
Shahin, M. Y., Nunez, M. M., Broten, M. R., Carpenter, S. H., and Sameh, A. (1987). “New techniques for modeling pavement deterioration.” Transportation Research Record. 1123, Transportation Research Board, Washington, D.C., 40–46.
Tavakoli, A., Lapin, M., and Figueroa, J. L. (1992). “PMSC: Pavement management system for small communities.” J. Transp. Eng., 118(2), 270–280.
Way, G. B., Eisenberg, J., and Kulkarni, R. B. (1982). “Arizona pavement management system: Phase 2, verification of performance prediction models and development of data base.” Transportation Research Record. 846, Transportation Research Board, Washington, D.C., 49–55.
Information & Authors
Information
Published In
Copyright
© 2007 ASCE.
History
Received: Nov 28, 2005
Accepted: Jul 27, 2006
Published online: Jun 1, 2007
Published in print: Jun 2007
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.