摘要: |
The rate of pavement deterioration is uncertain, and a pavement management system (PMS) should portray this rate of deterioration as uncertain. A wide variety of PMSs are used, but unfortunately either these systems do not use a formalized procedure to determine the pavement condition rating, or they use deterministic pavement performance prediction models, or they assign the pavement state transition probabilities on the basis of experience. The objective of the research was to develop a probabilistic network-level PMS on the basis of pavement performance prediction with use of the Markov process. Pavements with similar characteristics are grouped together to define the pavement families, and the prediction models are developed at a family level. The pavement condition index (PCI), ranging from 0 to 100, is divided into 10 equal states. The results from the Markov model are fed into the dynamic programming model and the output from the dynamic programming is a list of optimal maintenance and repair (M&R) recommendations for each pavement family-state combination. If there are no constraints on the available budget, the M&R recommendations from the dynamic programming will give a true, optimal budget. However, because the budgets available are usually less than the needs, two prioritization programs have been developed to allocate the constrained budgets in an optimal way. The first prioritization program is based on simple ranking of the weighted optimal benefit/cost ratios, and the second is based on the incremental benefit/cost ratio. The output from the two programs is a list of sections to be repaired, type of M&R alternatives selected, cost of M&R alternatives, and section network benefits. The results from the two prioritization methodologies are compared through an actual implementation on an existing airfield pavement network. The prioritization using the incremental benefit/cost ratio program uses the available constrained budget to the best of the full limit. To maintain a specified network PCI, the optimal benefit/cost ratio program will spend less money than the incremental benefit/cost ratio program. The developed optimization programs are very dynamic and robust for network-level PMSs. |