NSF Award
2318641
Wastewater infrastructures are critical for modern cities, but aging sanitary sewer systems often suffer from defects like cracked pipes and damaged manholes, leading to the infiltration and inflow problem. This problem results in excessive surface runoff and groundwater flowing into the sewer system, causing sewer overflows and posing risks to public health and the environment. Current management approaches require significant time and expensive resources. This research project addresses this challenge by combining Graph Neural Networks and in-situ water pressure monitoring. The Graph Neural Networks surrogate model represents the urban wastewater system as a graph, allowing for efficient modeling of its temporal, spatial, and topological properties. Further by integrating a physical sensing system, this research enables accurate infiltration and inflow anomaly detection and predictions of cascading impacts with the Graph Neural Networks backbone allowing proactive and corrective actions to be taken in time.<br/><br/>This research revolutionizes the management of urban wastewater systems by leveraging the interdisciplinary knowledge and expertise from hydrological & hydraulic sciences, embedded systems, and artificial intelligence. The technical outcomes of this research can significantly enhance public safety and health in coastal regions by improving the resilience of urban wastewater systems to climate change effects and facilitating quick recovery after natural hazards. By exploring the development of a fully sensed digital twin of the targeted wastewater system in south Texas, the project advances towards increased understanding and management capabilities of wastewater systems. The research results are closely integrated into the education and training of students. Besides, this project promotes the participation of underrepresented groups and K-12 students to pursue STEM studies. All designs are made publicly available to ensure equitable access to Artificial Intelligence-powered decision-making tools for broad implications and future research advances.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
