Digital Twin-Enabled Predictive Maintenance for Smart City Water Distribution Networks Using Physics-Informed Neural Networks

Abstract

Aging water distribution infrastructure in cities worldwide loses 30-50% of treated water through leaks and pipe bursts, costing utilities over $39 billion annually. We present AquaTwin, a digital twin framework for urban water networks that integrates physics-informed neural networks (PINNs) with real-time IoT sensor data (flow, pressure, acoustic) to predict pipe failure probability with 72-hour lead time. Deployed in a 2,400 km pipe network serving 3.2 million people, AquaTwin achieved 82% precision in predicting pipe bursts over 12 months, reducing unplanned emergency repairs by 56% and non-revenue water from 38% to 24%. The PINN architecture enforces conservation of mass and energy (Hazen-Williams equations) as soft constraints, enabling accurate predictions even in sensor-sparse network segments.