Data-driven pavement maintenance and rehabilitation strategies for a new state route prioritization system
Gardner, Lauren Jessica
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GDOT, which is responsible for a large network of primarily asphalt pavements, has been utilizing a model developed by Georgia Tech under Research Project 05-19 for its current performance analyses. While the model provides an adequate analysis of pavement performance within the network and adheres to both the MAP-21 and the FAST Act, the current model has not been updated in nearly 10 years. Because the existing model is a probabilistic model, the prediction power of the model suffers without the use of current data. Additionally, the GDOT has implemented a new policy that identifies the priority of pavement projects based on importance and utilization. The introduction of the new policy provides an opportunity to better categorize the current pavement system to maximize utilization of the GDOT’s resources for maintenance. Advances in segment-level survey collection documentation also provides a potential area of improvement as the data enables Maintenance, Rehabilitation, and Reconstruction (MR&R) trigger criteria to be studied. By utilizing new techniques, policies, and data, the existing model used by GDOT for the PMS can be improved. Questions about improved data processing, model development using pavement priority categories, and trigger criteria information can be answered by updating the previous model and utilizing the COPACES system. The results should be able to more efficiently and cost-effectively manage GDOT’s pavement preservation planning at a network level.