University of Pittsburgh

The University of Pittsburg team will pursue a new vision for in-situ repair and rehabilitation of pipelines with value added embedded sensing to complement existing non-destructive evaluation (NDE) and in-line inspection techniques. The team will demonstrate robotically deployable cold spray-based processes for producing a metallic pipe within the original structure and explore the feasibility of embedded fiber optic sensors within the newly constructed internal pipe. Acoustic NDE methods will be coupled with embedded fiber optic sensors as well as machine learning-based frameworks to identify, localize, and classify pipeline defects such as corrosion and other operational conditions that require maintenance and repair. Cold spray-based metallic coating offers a scalable and practical solution to confined space repair and is anticipated to meet necessary standards and regulatory approvals. Sensor embedding with the cold spray repair process combined with information available through in-line inspections, mapping, and NDE in the context of artificial intelligence (AI)-based classification frameworks can ultimately reduce downtime associated with major repairs and avoid costly, catastrophic failures. If successful, the team anticipates potential for additional efforts focused on robotic deployment strategies for cold spray rehabilitation and sensor embedding as well as integration of the AI-classification framework, new fiber optic sensor capabilities, and other available information into a digital twin model of the pipeline network.

REPAIR seeks to eliminate the highest pipe rehabilitation costs, excavation and restoration, by repairing pipes without removal.