🤖 AI Summary
This study addresses the challenge of insufficient subsurface pipeline condition awareness in data-scarce regions, such as the U.S. Virgin Islands, which hinders effective inspection and maintenance decisions. The authors propose a repair-oriented decision-making framework for water distribution network maintenance, formulating the problem as a discounted Markov decision process coupled with high-fidelity hydraulic simulation. Relying solely on readily available system-level observations, the framework infers latent pipe conditions by establishing a unique mapping between observable system dynamics and failures in specific pipe segments, thereby enabling virtual sensing without segment-level instrumentation. The approach explicitly captures heterogeneous failure characteristics across pipe segments and generates state-dependent optimal maintenance policies, demonstrating the feasibility of dynamic-system-based, resource-efficient inspection planning under constrained conditions.
📝 Abstract
We develop a repair-oriented inspection and maintenance decision framework for water distribution networks. This work is motivated by utilities operating in data-sparse environments, such as in remote locations like the U.S. Virgin Islands, where data collection about network state and underground pipeline outages is limited to above-ground and easy to access information (e.g., water tank levels and pump operations). We formulate the problem as a discounted Markov decision process and integrate it with high-fidelity hydraulic simulation. The model captures latent system dynamics without requiring pipe-level sensing. The results reveal state-dependent optimal policies and heterogeneous failure characteristics across pipes, including rare but high-impact behaviors. We further show that certain observable system states uniquely correspond to specific pipe failures, enabling a form of virtual sensing. These findings demonstrate that system-level dynamics can support inspection planning and maintenance decisions under uncertainty in resource-constrained settings.