Infrastructure systems are critical to sustaining and improving economical growth. Poor condition of infrastructure systems results in lost productivity and reduces the quality of life. Today's global economy forces governments to sustain and renew infrastructure systems already in place in order to remain competitive and productive (GAO, 2008). Therefore, civil engineers and policymakers have been quite interested in the overall quality of the highways and bridges throughout the US (Miller, 2007). Transportation networks are essential parts of the Nation's infrastructure systems. Deterioration due to age and use is the main threat to the level of service observed in surface transportation networks. Thus, highway agencies throughout the United States strive to maintain, repair and renew transportation systems already in place (Miller, 2007). A recent disaster, the collapse of the Minneapolis I-35 W Bridge, once again revealed the importance of infrastructure preservation programs and resulted in debates as to how state departments of transportation (DOTs) should and can preserve the existing infrastructure systems. Therefore, it is essential to establish effective maintenance programs to preserve aging infrastructure systems.

The major challenge facing the state highway maintenance managers today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives, which makes the situation even more challenging. Insufficient use of available smart decision-making tools impedes eliciting effective and efficient maintenance programs. Hence, this thesis presents the development and implementation of a network-level pavement maintenance optimization model which can be used by maintenance managers as a decision-making tool to address the maintenance budget allocation issue.

The network-level optimization model is established with the application of the Linear Programming algorithm and is subject to budget constraints and the agencies' pavement performance goals in terms of total lane-miles in each pavement condition state. This tool is developed with Microsoft Office Excel. The tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Thus, the model enables maintenance managers in highway agencies to develop alternative network-level pavement maintenance strategies through an automated and optimized process rather than using what-if analysis.