PI: Matteo Pozzi, Civil and Environmental Engineering
Co-PI(s): Roja Malligarjunan, Civil and Environmental Engineering
In this project, we will collaborate with industrial partner RedZone Robotics to integrate their robotic inspection and data management services with probabilistic adaptive infrastructure performance models in order to optimize the management of wastewater collection systems. These systems are integral to the functioning of modern cities, but many are aging and subjected to increasing stresses, leading to declining performance. Uptodate information on pipeline states can be efficiently obtained via robotic inspection efforts with autonomous sensor platforms. This data can also be processed using adaptive probabilistic models of pipeline conditions and deterioration processes. Together, timely pipe condition data coupled with predictive condition models can be used to better understand the current and future conditions of a wastewater system. Furthermore, probabilistic modeling of infrastructure conditions can guide future robotic inspector deployments to areas of greatest need, such as where pipe failures are predicted to occurs, or greatest uncertainty, where the model predictions are most unreliable based on current information. By collaborating with RedZone Robotics on this project, we will have access to large volumes of high-quality pipeline inspection information from a variety of municipalities, which will allow us to calibrate robust models of pipeline performance which will be applicable across a wide variety of system. We expect to develop a suite of modeling and optimization tools which will allow RedZone Robotics to provide a high level of service to their customers, reducing their wastewater pipeline inspection and management costs and allowing the company to expand its customer base. This project will also advance the position of Pennsylvania as an incubator for smart infrastructure and smart city technologies, showcasing innovative applications of robotics and data mining to support optimized management of resources for infrastructure management and encouraging similar activities and collaborative innovations in the future.