Abstract:
A Pavement Management System (PMS) is a tool that aids in determining the most effective application of maintenance and reconstruction (M&R) work for a given pavement network. The purpose of this research is the development and implementation of an Airfield Pavement Management System for the Winnipeg International Airport (WIA) that expands on the capabilities and usefulness of conventional systems. The system developed in this research utilizes geo-referenced pavement distress data collected using a GPS receiver. Time required to complete Pavement Condition Index surveys has been reduced, minimizing the impact on airport operations of conducting an airfield inspection. Using geo-referenced distress data leads to a multitude of new analysis techniques that allow for optimum management of the pavement network. Several pavement deterioration models have been created using the collected pavement condition data. These models aim to predict pavement condition at points in the future to aid in M&R planning. Models were created using the least-squares regression technique as well as neural network modelling. The use of neural networks appears promising as they are not constrained to a single regression parameter and can account for the interaction b etween parameters and nonlinear relationships. The WIA PMS represents a significant improvement to the functionality of current PMSs by expanding the analysis and modelling capabilities while reducing the effort associated with data collection.
