Optimization of Sensor Placement for Passive Pipeline Deterioration Monitoring Using Ambient Noise Correlation

Abstract Passive monitoring based on the correlation of ambient noise enables the estimation of pipeline wave speed and the assessment of pipe wall deterioration without active hydraulic transient generation. To scale this approach to large water distribution networks, efficient sensor placement is essential. This study develops an optimization framework that maximizes monitoring coverage while minimizing sensor count. Practical deployment constraints, including dual-peak visibility, prioritization of critical pipes, and installation at feasible access points, are incorporated. A customized genetic algorithm is applied to identify Pareto-optimal tradeoffs. Simulations on branched-looped networks show that the framework achieves full coverage under unconstrained conditions and adapts effectively to more restrictive deployment scenarios. The results demonstrate that combining passive acoustic sensing with optimization supports cost-effective and scalable deterioration monitoring for smart water networks.