The administration of transportation infrastructure entails addressing a multitude of obstacles arising from the intricate, fast changing and dynamic nature of the environment. This thesis focuses on improving infrastructure maintenance planning through the application of deep reinforcement learning. The research begins with a thorough examination of current approaches in transportation network simulation, revealing the omissions in current strategies for infrastructure upkeep. This is followed by the development of a simulation environment that simulates realworld conditions and integrates pavement and bridge condition models to evaluate various maintenance strategies.
The proposed framework is tested on a simulated transportation network in the United States, which incorporates a traffic model to account for dynamic changes. The DRL algorithms are then used to make maintenance policies for this environment. These policies are evaluated through extensive simulations, with a focus on reducing maintenance expenses and improving the overall condition of the infrastructure.