  Large-scale infrastructure systems are crucial for societal welfare, and
their effective management requires strategic forecasting and intervention
methods that account for various complexities. Our study addresses two
challenges within the Prognostics and Health Management (PHM) framework applied
to sewer assets: modeling pipe degradation across severity levels and
developing effective maintenance policies. We employ Multi-State Degradation
Models (MSDM) to represent the stochastic degradation process in sewer pipes
and use Deep Reinforcement Learning (DRL) to devise maintenance strategies. A
case study of a Dutch sewer network exemplifies our methodology. Our findings
demonstrate the model's effectiveness in generating intelligent, cost-saving
maintenance strategies that surpass heuristics. It adapts its management
strategy based on the pipe's age, opting for a passive approach for newer pipes
and transitioning to active strategies for older ones to prevent failures and
reduce costs. This research highlights DRL's potential in optimizing
maintenance policies. Future research will aim improve the model by
incorporating partial observability, exploring various reinforcement learning
algorithms, and extending this methodology to comprehensive infrastructure
management.
