An Integrated Tool for Optimizing Rehabilitation Programs of Highways Pavement

Mohamed Marzouk, Ehab Awad, Moheeb El-Said


Modeling pavement performance and optimizing resources represent two challenges for decision makers responsible for maintenance and rehabilitation of road networks pavement. This paper presents the developments made in a stochastic performance prediction model and optimization model as two major parts of an integrated pavement management system. Markov modeling is used to create a transition process model that is implemented to predict pavement condition throughout the life time of road networks. With the use of the Pavement Condition Index (PCI), the steps of performing the prediction of deterioration are presented, showing the process of creating the elements of Markov matrix. The obtained results are used to set the priorities for maintenance planning and budgeted cost allocations on the network level. The proposed model advises decision makers on the status of network level with the guidelines to keep road conditions in acceptable level of performance according to the predefined strategies. Genetic algorithms technique is adopted to build optimization model. Three objective functions are constructed for budgeted cost of maintenance and rehabilitation program, quality of work performed, and selected area for program implementation. A brief description of the developed pavement management systems, including the prediction and the optimization models, are presented. A numerical example is worked out to illustrate the practical use of both models.


pavement management system; Markov modeling; multi-objective optimization; Pareto front; genetic algorithms

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DOI: 10.3846/bjrbe.2012.39


1. Network-Based Optimization System for Pavement Maintenance Using a Probabilistic Simulation-Based Genetic Algorithm Approach
Amr A. Elhadidy, Emad E. Elbeltagi, Sherif M. El-Badawy
Journal of Transportation Engineering, Part B: Pavements  vol: 146  issue: 4  first page: 04020069  year: 2020  
doi: 10.1061/JPEODX.0000237


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