Deep reinforcement learning for multi-asset infrastructure management incorporating traffic operations adaptations and control

This project will develop methodologies to quantify the effects of both system and statistical correlations in I&M decision-making processes by integrating advanced statistical learning and inference methods with machine learning and artificial intelligence algorithms in a synergistic computational platform. Although statistical correlations have been well studied in the literature of decision theory and optimal planning in multi-component and distributed systems, these methods require further advances for their application in transportation asset management. Comprehensive system correlation studies and practices are largely lacking, mainly due to the emerging dimensionality issues that arise in network-level infrastructure management.