Deep reinforcement learning : an analytical tool to design and maintain environmentally benign pavement infrastructure

Pavement life cycle assessments (LCAs) enable decision-makers to evaluate the environmental impact of alternative maintenance, rehabilitation, and reconstruction strategies. This thesis explores the viability of deep reinforcement learning (DRL), a framework that enables agents to learn optimal actions within a given situation, to identify environmentally benign pavement management strategies. More specifically, this dissertation utilizes proximal-policy optimization (PPO), a subtype of DRL algorithms, to identify a management strategy that minimizes the expected global warming impact of a pavement facility over its lifecycle. Through an urban Interstate case study, this thesis shows that the proposed PPO algorithm identifies management strategies that are anticipated to reduce the expected global warming impact of a pavement facility over its planning horizon by 16 percent relative to traditional practice. Furthermore, the PPO algorithm is able to identify this management strategy in only 25 learning iterations, which is in stark comparison to Q-learning, a common reinforcement learning algorithm, that requires 70,000 learning iterations. The results of this thesis highlight the viability of DRL to integrate within complex LCA models to determine environmentally sustainable pavement management strategies.Applied Science, Faculty ofCivil Engineering, Department ofGraduat